FI MU Study Catalogue 2022/2023
Study catalogue in allinone version
The FI MU Study Catalogue is a document describing the conditions of study at the Faculty of Informatics in Bachelor's and Followup Master's Degree Programs, which are valid for students who have started their studies in one of those study programs in the given academic year. Faculty of Informatics is committed to preserve these conditions as much as possible during the whole period of studies.
Bachelor's Degree Programs
Followup Master's Degree Programs (English)
Bachelor's Degree Programs
bachelor's program without specializations supporting Major/Minor study
This study programme is recommended to students who intend to get fundamental knowledge in informatics and get acquainted with the general principals of making and using information technology. Besides, the basic orientation in the field students will get enough knowledge and practical training to be able to find employment in the field immediately after graduation. The programme offers some options to aim the profile of the education towards selected basic areas of computer science, such as computer graphics, data processing, information security, networking, artificial intelligence, and computer science.
Graduates may immediately start working on junior IT positions and will be ready to deepen their knowledge according to the needs of their employer. Graduates are also ready to continue their studies in any master degree programme related to informatics or to opt for some other discipline to get interesting interdisciplinary knowledge.
Requirements for successful graduation
 Obtain at least 180 credits overall and pass the final state exam.
 Obtain 10 credits for SBAPR subject and successfully defend Bachelor's Thesis. See more details.
 Fulfil requirements of a singlefield study option or Major study option.
 Pass all the compulsory and elective courses of the program, selected study option, and selected focus with the highest possible graduation form.
 Obtain at least two credits from Physical training. See University Sport Centre.
Compulsory subjects of the program
IB000 
Mathematical Foundations of Computer Science 

IB002 
Algorithms and data structures I 
IB005 
Formal Languages and Automata 
IB015 
NonImperative Programming 
IB111 
Foundations of Programming 
MB151 
Linear models 
MB152 
Differential and Integral Calculus 
MB153 
Statistics I 
MB154 
Discrete mathematics 
PB006 
Principles of Programming Languages and OOP 
PB007 
Software Engineering I 
PB071 
Principles of lowlevel programming 
PB151 
Computer Systems 
PB152 
Operating Systems 
PB152zk 
Operating Systems  Exam 
PB154 
Database Systems 
PB156 
Computer Networks 
PV004 
UNIX 
PV080 
Information security and cryptography 
VB001 
English Exam 
SBPrip 
Revisions for Bachelor State Exam 
SOBHA 
Defence of Thesis 
SZB 
State Exam (Bc degree) 
Typesetting and academic writing Pass at least 1 course of the following list  
VB000

Elements of Style 
VB000Eng

Introduction to Academic Writing 
PB029

Electronic Document Preparation 
English Obtain at least 2 credits by passing subjects of the following list  
VB035

English I 
VB036

English II 
VV064

Academic and Professional Skills in English for IT 
Common university background Obtain at least 10 credits by passing subjects of the following list  
IV123

InformaticsDriven Future 
VB000

Elements of Style 
VB000Eng

Introduction to Academic Writing 
PB029

Electronic Document Preparation 
VB003

Economic Style of Thinking I 
VB004

Economic Style of Thinking II 
VB005

Panorama of Physics I 
VB006

Panorama of Physics II 
VB007

Philosophy of Science I 
VB008

Philosophy of Science II 
VB010

Philosophy of Language and Its Problems I 
VB011

Philosophy of Language and Its Problems II 
VB023

Folk Music 
VB041

The Principles of Legal Reasoning 
VB042

Intellectual Property 
VV014

Religionistics 
VV015

Political Science 
VV018

Topics in Religionistics 
VV019

Selected chapters in politology 
VV024

Interpretation of Texts 
VV026

Creative Writing Workshop 
VV027

Postmodern culture 
VV028

Psychology in Informatics 
VV030

Philosophy and Theories of the Mind 
VV031

Basics of Fine Art I 
VV032

Introduction to History of Fine Art II 
VV040

Theater Play 
VV063

Computers and Ergonomy 
VV065

Selected chapters in the Theory of Mind 
VV066

English Conversation 
VV076

Ethics and Information Technology 
VV077

Business English for career in IT 
CORE*

Courses with prefix CORE 
Study option: Singlefield study of Informatics
Compulsory subjects and other obligations of the study option
Pass all obligatory courses of the program.  
IB107 
Computability and Complexity 

IB031 
Introduction to Machine Learning 
PB016 
Introduction to Artificial Intelligence 
Programming Pass at least 1 course of the following list  
PB161

C++ Programming 
PB162

Java 
PV178

Introduction to Development in C#/.NET 
Fulfil the conditions of at least one focus group. 
Focus groups
General Informatics
This choice is recommended to students who intend to stay without any focus in their Bachelors' degree study.
PV065 
UNIX  Programming and System Management I 

MV008 
Algebra I 
PB009 
Principles of Computer Graphics 
PV003 
Relational Database System Architecture 
IB109 
Design and Implementation of Parallel Systems 
PV028 
Applied Information Systems 
PV005 
Computer Network Services 
Computer Systems, Communication and Security
This focus is recommended to students who intend to continue their studies in followup Masters' degree program Computer Systems, Communication and Security.
PV170 
Design of Digital Systems 

PV065 
UNIX  Programming and System Management I 
PB138 
Modern Markup Languages and Their Applications 
PV077 
UNIX  Programming and System Management II 
PV005 
Computer Network Services 
IB109 
Design and Implementation of Parallel Systems 
Choice in computer systems Pass at least 1 course of the following list  
PB176

Basics of Quality and Managment of Source Code 
PB173

Domain specific development 
Visual Informatics
This focus is recommended to students who intend to continue their studies in followup Masters' degree program Visual Informatics.
PB130 
Introduction to Digital Image Processing 

PB009 
Principles of Computer Graphics 
PV112 
Computer Graphics API 
PV131 
Digital Image Processing 
Choice in visual informatics Obtain at least 2 credits by passing subjects of the following list  
PV160

Laboratory of HumanComputer Interaction 
PV162

Image Processing Project 
Graphic Design
This focus is recommended to students who intend to continue their studies in followup Masters' degree program Visual Informatics specialized in Graphic Design.
PB130 
Introduction to Digital Image Processing 

PV123 
Introduction to Visual Communication 
PB009 
Principles of Computer Graphics 
PV078 
Graphic Design I 
PV272 
3D Modelling 
PV066 
Typography I 
PV131 
Digital Image Processing 
PV084 
Type Design I 
Bioinformatics and System Biology
This focus is recommended to students who intend to continue their studies in followup Masters' degree program Artificial Intelligence and Data Processing specialized in Bioinformatics and System Biology.
IV107 
Bioinformatics I 

VV071 
Biochemistry for bioinformatics 
PA052 
Introduction to Systems Biology 
VV072 
Molecular biology for bioinformatics 
IV114 
Bioinformatics and Systems Biology Project 
PB051 
Computational methods in Bioinformatics and Systems Biology 
Math Informatics
This focus is recommended to students who intend to continue their studies in followup Masters' degree program Theoretical Computer Science or followup Masters' degree program Artificial Intelligence and Data Processing.
MV008 
Algebra I 

IV109 
Modeling and Simulation 
IV119 
Seminar on Discrete Mathematical Methods 
MA010 
Graph Theory 
MA018 
Numerical Methods 
Natural Language Processing
This focus is recommended to students who intend to continue their studies in followup Masters' degree program Artificial Intelligence and Data Processing specialized in Natural Language Processing.
MV008 
Algebra I 

IB030 
Introduction to Computational Linguistics 
IB047 
Introduction to Corpus Linguistics and Computer Lexicography 
PB095 
Introduction to Speech Processing 
PB106 
Corpus Linguistic Project I 
PV173 
Natural Language Processing Seminar 
Extended math education
When selecting this option, the obligation of courses with prefix MB is cancelled. This focus is recommended to students who intend to continue their studies in followup Masters' degree program Theoretical Computer Science or followup Masters' degree program Artificial Intelligence and Data Processing.
PřF:MIN101 
Mathematics I 

PřF:M1VM01 
Algorithmization and numerical computations 
PřF:MIN201 
Mathematics II 
PřF:MIN202 
Numerical calculations 
PřF:MIN301 
Mathematics III 
PřF:MIN401 
Mathematics IV 
PřF:M3121 
Probability and Statistics I 
PřF:M4122 
Probability and Statistics II 
Fundaments of mathematics
When selecting this option, the obligation of courses with prefixes MB151 and MB152 is cancelled. This focus is recommended to students who intend to continue their studies in followup Masters' degree program Theoretical Computer Science or followup Masters' degree program Artificial Intelligence and Data Processing.
PřF:M1110 
Linear Algebra and Geometry I 

PřF:M2110 
Linear Algebra and Geometry II 
PřF:M1100 
Mathematical Analysis I 
PřF:M2100 
Mathematical Analysis II 
PřF:M2150 
Algebra I 
Choice in advanced mathematics Pass at least 1 course of the following list  
PřF:M3150

Algebra II 
PřF:M3100

Mathematical Analysis III 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Spring 2024 (4. term)
Fall 2024 (5. term)
Study option: Major
Compulsory subjects and other obligations of the study option
Pass all obligatory courses of the program.  
Fulfill conditions of Minor of another study program. 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Spring 2024 (4. term)
Fall 2024 (5. term)
Study option: Minor
Compulsory subjects and other obligations of the study option
IB000 
Mathematical Foundations of Computer Science 

IB110 
Introduction to Informatics 
IB113 
Introduction to Programming and Algorithms 
IB114 
Introduction to Programming and Algorithms II 
PB001 
Introduction to Information Technologies 
PB007 
Software Engineering I 
PB153 
Operating Systems and their Interfaces 
PB156 
Computer Networks 
PB168 
Introduction to DB and IS 
PV004 
UNIX 
PV157 
Authentication and Access Control 
IV130 
Pros and Cons of Intelligent Systems 
IV109 
Modeling and Simulation 
SZB 
State Exam (Bc degree) 
Recommended course of study
Fall 2023 (3. term)
Spring 2024 (4. term)
Fall 2024 (5. term)
bachelor's program without specializations
The focus of the Programming and development bachelor program is design, creation, implementation, and program maintenance technology and in lesser amount also technical equipment of modern computer systems and digitally controlled systems. Graduates of the program will have a fundamental understanding of the whole computer systems life cycle, starting with computer architectures, programming and software engineering, through computer networks and operating systems and ending with the development of embedded systems. This technological view is supported by the necessary mathematical foundations and by an introduction to design principles of secure computer systems. An important feature of the program is the focus on continuous practical verification of attained knowledge, including semestral project and voluntary semesterlong internship. The goal of this program is to focus the graduates on the solving the technological (real world) problems.
Graduates are able to immediately work as junior programmers, designers or members of a test team with fundamentals broad enough for following professional and career growth.
Requirements for successful graduation
 Obtain at least 180 credits overall and pass the final state exam.
 Obtain 10 credits for SBAPR subject and successfully defend Bachelor's Thesis. See more details.
 Pass all the compulsory and elective courses of the program with the highest possible graduation form.
 Obtain at least two credits from Physical training. See University Sport Centre.
Compulsory subjects of the program
IB000 
Mathematical Foundations of Computer Science 

IB002 
Algorithms and data structures I 
IB015 
NonImperative Programming 
IB109 
Design and Implementation of Parallel Systems 
IB110 
Introduction to Informatics 
IB111 
Foundations of Programming 
PB006 
Principles of Programming Languages and OOP 
PB007 
Software Engineering I 
PB071 
Principles of lowlevel programming 
PB138 
Modern Markup Languages and Their Applications 
PB151 
Computer Systems 
PB152 
Operating Systems 
PB152cv 
Operating Systems  practicals 
PB154 
Database Systems 
PB156 
Computer Networks 
PB156cv 
Computer Networks  practicals 
PB175 
Project managment and project 
PV004 
UNIX 
PV028 
Applied Information Systems 
PV080 
Information security and cryptography 
PV170 
Design of Digital Systems 
MB141 
Linear algebra and discrete mathematics 
MB142 
Applied math analysis 
MB143 
Design and analysis of statistical experiments 
VB000 
Elements of Style 
VB001 
English Exam 
SBPrip 
Revisions for Bachelor State Exam 
SB100 
Bachelor Internship  Programming and Development 
SOBHA 
Defence of Thesis 
SZB 
State Exam (Bc degree) 
Programming 1 Pass at least 1 course of the following list  
PB161

C++ Programming 
PB162

Java 
Programming 2 Pass at least 1 course of the following list  
PB173

Domain specific development 
PV168

Seminar in Java programming 
PV178

Introduction to Development in C#/.NET 
PV256

Introduction to Development for Android 
English Obtain at least 2 credits by passing subjects of the following list  
VB035

English I 
VB036

English II 
VV064

Academic and Professional Skills in English for IT 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Spring 2024 (4. term)
Fall 2024 (5. term)
bachelor's program without specializations supporting Major/Minor study
The aim of this bachelor's study program is to equip applicants with the necessary professional knowledge and the necessary minimum of psychologicalpedagogical knowledge for successful work in education in the field of informatics. The program is also a program that in combination with a followup teaching program at MU, prepares graduates for the teaching profession. The degree is open only in the minor version in cooperation with the degrees of the Faculty of Science of Masaryk University.
The graduate is ready to continue studying in a followup teaching program at MU or can work in various training centers with a focus on IT training.
Requirements for successful graduation
 Obtain at least 180 credits overall and pass the final state exam.
 Obtain 10 credits for SBAPR subject and successfully defend Bachelor's Thesis. See more details.
 Pass all the compulsory and elective courses of the selected study option with the highest possible graduation form.
Study option: Minor
Compulsory subjects and other obligations of the study option
IB000 
Mathematical Foundations of Computer Science 

IB110 
Introduction to Informatics 
IB113 
Introduction to Programming and Algorithms 
IB114 
Introduction to Programming and Algorithms II 
PB151 
Computer Systems 
PB153 
Operating Systems and their Interfaces 
PB156 
Computer Networks 
PV157 
Authentication and Access Control 
PB007 
Software Engineering I 
PB168 
Introduction to DB and IS 
UB001 
Assesment of teaching in Informatics 
SBPrip 
Revisions for Bachelor State Exam 
Programming Pass at least 1 course of the following list  
PB161

C++ Programming 
PB162

Java 
PB071

Principles of lowlevel programming 
Application development Pass at least 1 course of the following list  
PB069

Desktop Application Development in C#/.NET 
PB138

Modern Markup Languages and Their Applications 
PV256

Introduction to Development for Android 
Collect at least 70 credits from courses tought at FI with prefixes IB, IB, PB, or PV. 
Recommended course of study
Spring 2023 (2. term)
Fall 2023 (3. term)
Spring 2024 (4. term)

IB110
Introduction to Informatics  Choice: Any course from Programming section
Fall 2024 (5. term)
bachelor's program without specializations
The program will meet the growing interest of both high school graduates and already employed jobseekers without formal education in the field who carry out professions where knowledge and skills in cybersecurity.
Graduates will be ready for a professional of system administrators, operators in information security operations center, CSIRT team members, lower or middle management in cybersecurity; software engineers of securityrelevant IT applications and systems, as well as cybersecurity trainers or assistants to cybersecurity managers.
Requirements for successful graduation
 Obtain at least 180 credits overall and pass the final state exam.
 Obtain 10 credits for SBAPR subject and successfully defend Bachelor's Thesis. See more details.
 Pass all the compulsory and elective courses of the program with the highest possible graduation form.
 Obtain at least two credits from Physical training. See University Sport Centre.
Compulsory subjects of the program
MB141 
Linear algebra and discrete mathematics 

IB000 
Mathematical Foundations of Computer Science 
IB110 
Introduction to Informatics 
IB113 
Introduction to Programming and Algorithms 
IB114 
Introduction to Programming and Algorithms II 
PB007 
Software Engineering I 
PB071 
Principles of lowlevel programming 
PB151 
Computer Systems 
PB152 
Operating Systems 
PB152cv 
Operating Systems  practicals 
PB156 
Computer Networks 
PB156cv 
Computer Networks  practicals 
PB168 
Introduction to DB and IS 
PV004 
UNIX 
PV028 
Applied Information Systems 
PV080 
Information security and cryptography 
IV130 
Pros and Cons of Intelligent Systems 
PV157 
Authentication and Access Control 
PV175 
MS Windows Systems Management I 
PV276 
Seminar on Simulation of Cyber Attacks 
VB000 
Elements of Style 
VB001 
English Exam 
SB200 
Bachelor Internship  Cybersecurity 
PrF:BI301K 
ICT Law II 
PrF:BVV03K 
Cybercriminality 
FSS:BSSb1101 
Introduction into Security and Strategic Studies 
FSS:BSSb1103 
Security Policy of the Czech Republic 
FSS:BSSb1152 
Cyber Warfare 
Programming Pass at least 1 course of the following list  
PB161

C++ Programming 
PB162

Java 
Cybersecurity Pass at least 1 course of the following list  
PV017

Information Technology Security 
PV210

Cybersecurity in an Organization 
English Obtain at least 2 credits by passing subjects of the following list  
VB035

English I 
VB036

English II 
VV064

Academic and Professional Skills in English for IT 
SBPrip 
Revisions for Bachelor State Exam 
SOBHA 
Defence of Thesis 
SZB 
State Exam (Bc degree) 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)

PrF:BI301K
ICT Law II 
FSS:BSSb1152
Cyber Warfare 
PB007
Software Engineering I 
PV028
Applied Information Systems 
PV175
MS Windows Systems Management I
Spring 2024 (4. term)
Fall 2024 (5. term)
Followup Master's Degree Programs (Czech)
followup master's program (Czech) with specializations
The study of theoretical computer science focuses on a deeper understanding of basic principles underpinning the development of contemporary information technologies, including nonclassical computational devices such as neural networks or quantum computers. Together with the active mastering of advanced theoretical as well as practical concepts, a special emphasis is put on the development of abstract thinking. The students gain a deeper understanding of advanced algorithms, principles of modern programming languages, and methods for verification and analysis of computer programs. Further, they understand the basic advantages and limitations of nonclassical computational devices. After successfully completing the programme, the students are qualified for a wide variety of positions requiring complex expert skills.
After successfully completing the study programme, the students are qualified for a variety of IT positions including a developer, system architect, or verification engineer. Solid mathematical skills together with deep knowledge of nontrivial algorithms enable the students to find jobs in the financial sector. The acquired knowledge and skills may be well used also in the followup Ph.D. programme.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
IA006 
Selected topics on automata theory 

IA008 
Computational Logic 
IA011 
Programming Language Semantics 
IA012 
Complexity 
IV003 
Algorithms and Data Structures II 
IV111 
Probability in Computer Science 
MA007 
Mathematical Logic 
PV021 
Neural Networks 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
Specialization: Discrete algorithms and models
Students specializing in Discrete Algorithms and Models will gain advanced knowledge in a wide range of areas of theoretical computer science and related areas of mathematics. Graduates of the specialization will be able to solve very demanding tasks from selected areas of theoretical computer science and will have basic experience with scientific work similar to doctoral studies.
Compulsory subjects of the specialization
IA168 
Algorithmic game theory 

MA010 
Graph Theory 
PV027 
Optimization 
MA009 
Algebra II 
Advanced mathematics Pass at least 1 course of the following list  
IA062

Randomized Algorithms and Computations 
PřF:M8190

Number Theoretic Algorithms 
MA017

Geometric Algorithms 
MA015

Graph Algorithms 
Choice of Seminar Obtain at least 4 credits by passing subjects of the following list  
IA072

Seminar on Verification 
IV115

Parallel and Distributed Laboratory Seminar 
IV131

Seminar of Discrete Methods and Algorithms Laboratory 
IV125

Formela lab seminar 
IA174 
Fundaments of Cryptography 
IA101 
Algorithmics for Hard Problems 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Quantum and other Nonclassical Computational Models
Specialization Quantum and other Nonclassical Computational Models will familiarize students with problem solving methods, which are computationally demanding on conventional computers. Graduates are also familiar with the principles, benefits and limitations of nonclassical computing systems such as neural networks or quantum computers.
Compulsory subjects of the specialization
IV100 
Parallel and distributed computations 

IA062 
Randomized Algorithms and Computations 
IA066 
Introduction to Quantum Computing 
IA082 
Physical concepts of quantum information processing 
IA101 
Algorithmics for Hard Problems 
IA174 
Fundaments of Cryptography 
PV056 
Machine Learning and Data Mining 
PV275 
Introduction to Quantum Computer Programming 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Formal Analysis of Computer Systems
The specialization Formal Analysis of Computer Systems focuses on formal methods for modeling, analysis, testing, and verification of computer programs as one of the basic building blocks of software systems development. Students get acquainted with the principles of modern verification tools and master practical skills required for working in teams responsible for ensuring the quality of the software products (quality assurance teams).
Compulsory subjects of the specialization
IA023 
Petri Nets 

IA159 
Formal Verification Methods 
IA168 
Algorithmic game theory 
IA169 
System Verification and Assurance 
IV120 
Continuous and Hybrid Systems 
PV260 
Software Quality 
Choice of Seminar Obtain at least 4 credits by passing subjects of the following list  
IA072

Seminar on Verification 
IV115

Parallel and Distributed Laboratory Seminar 
IV131

Seminar of Discrete Methods and Algorithms Laboratory 
IV125

Formela lab seminar 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Principles of Programming Languages
Specialization Principles of programming languages provide a deeper insight into the paradigms of modern programming languages and the structure of their compilers. Graduates can choose the optimal programming tools for a given application type and can quickly acquire new programming languages.
Compulsory subjects of the specialization
IA010 
Principles of Programming Languages 

IA014 
Advanced Functional Programming 
Advanced Types Pass at least 1 course of the following list  
IA038

Types and Proofs 
IA081

Lambda calculus 
IA158 
Real Time Systems 
IA174 
Fundaments of Cryptography 
IV010 
Communication and Parallelism 
PA008 
Compiler Construction 
PA037 
Compiler Project 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
followup master's program (Czech) with specializations
The Artificial Intelligence and Data Processing program prepares students to work in the areas of design and development of intelligent systems and analysis of big data. These areas are currently undergoing very fast development and are becoming increasingly important. The program leads students to a thorough understanding of basic theoretical concepts and methods. During the study students also solve specific case studies to familiarize themselves with the currently used tools and technologies. Students will thus gain experience that will allow them to immediately use the current state of knowledge in practice, as well as solid foundations, which will enable them to continue to independently follow the developments in the field. The program is divided into four specializations that provide deeper knowledge in a chosen direction. Specializations share a common core, where students learn the most important mathematical, algorithmic, and technological aspects of the field. Machine Learning and Artificial Intelligence specialization lead graduates to gain indepth knowledge of machine learning and artificial intelligence techniques and to gain experience with their practical application. Natural Language Processing specialization prepares graduates to work with natural languages (eg. Czech, English) in written and spoken form from the perspective of computer science. Data Management and Analysis specialization focus on data science, which creates value from big data by collecting, exploring, interpreting, and presenting data from different viewpoints with the goal of socalled business intelligence. Bioinformatics and Systems Biology specialization focuses on computational methods for automated analysis of large biological data and on creating predictive models of biological processes with the goal to better understand complex biological systems.
Due to the dynamic development of the area, the graduates have a wide range of career opportunities, with specific employment positions being created continuously during the course of their studies. Examples of different types of possible positions: positions in applied and basic research, typically concerning extensive data processing, often also in collaboration with experts from other disciplines such as biology or linguistics; positions in companies with an immediate interest in artificial intelligence and data processing (e.g., Seznam, Google) such as Data Scientist and Machine Learning Engineer; positions in companies that have extensive, valuable data (such as banking, telecom operators) or companies focusing on cloud data analysis, e.g., Business Intelligence Analyst or Data Analyst; graduates can also start their own startup specializing in the use of artificial intelligence methods in a particular area.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
MA012 
Statistics II 

IV126 
Fundamentals of Artificial Intelligence 
PA039 
Supercomputer Architecture and Intensive Computations 
PA152 
Efficient Use of Database Systems 
PV021 
Neural Networks 
PV056 
Machine Learning and Data Mining 
PV211 
Introduction to Information Retrieval 
PV251 
Visualization 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
Specialization: Machine Learning and Artificial Intelligence
Machine Learning and Artificial Intelligence specialization leads graduates to gain indepth knowledge of machine learning and artificial intelligence techniques and to gain experience with their practical application.
Compulsory subjects of the specialization
IV111 
Probability in Computer Science 

IA008 
Computational Logic 
PA163 
Constraint programming 
PA153 
Natural Language Processing 
PA228 
Machine Learning in Image Processing 
Applications of Machine Learning Pass at least 1 course of the following list  
PA167

Scheduling 
PA212

Advanced Search Techniques for Large Scale Data Analytics 
PA128

Similarity Searching in Multimedia Data 
PV254

Recommender Systems 
PA164

Machine learning and natural language processing 
IA168

Algorithmic game theory 
Projects and Laboratory Obtain at least 4 credits by passing subjects of the following list  
PA026

Artificial Intelligence Project 
PV115

Laboratory of Knowledge Discovery 
IV127

Adaptive Learning Seminar 
IV125

Formela lab seminar 
Optimizations and Numeric Computing Pass at least 1 course of the following list  
PV027

Optimization 
MA018

Numerical Methods 
PřF:M7PNM1

Advanced numerical methods I 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Data Management and Analysis
Data Management and Analysis specialization focuses on data science, which creates value from big data by collecting, exploring, interpreting, and presenting data from different viewpoints with the goal of so called business intelligence.
Compulsory subjects of the specialization
PA017 
Software Engineering II 

PA128 
Similarity Searching in Multimedia Data 
PA195 
NoSQL Databases 
PA200 
Cloud Computing 
PA212 
Advanced Search Techniques for Large Scale Data Analytics 
PA220 
Database systems for data analytics 
Data Algorithms Obtain at least 4 credits by passing subjects of the following list  
PA228

Machine Learning in Image Processing 
PV079

Applied Cryptography 
PA167

Scheduling 
PV254

Recommender Systems 
MA015

Graph Algorithms 
Projects and Laboratory Obtain at least 4 credits by passing subjects of the following list  
PV253

Seminar of DISA Laboratory 
PV115

Laboratory of Knowledge Discovery 
PV229

Multimedia Similarity Searching in Practice 
PA026

Artificial Intelligence Project 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Natural Language Processing
Natural Language Processing specialization prepares graduates to work with natural languages (eg. Czech, English) in written and spoken form from the perspective of computer science.
Compulsory subjects of the specialization
IA161 
Natural Language Processing in Practice 

IV111 
Probability in Computer Science 
PA153 
Natural Language Processing 
PA154 
Language Modeling 
PA156 
Dialogue Systems 
Math Pass at least 2 courses of the following list  
MA007

Mathematical Logic 
IA008

Computational Logic 
MA010

Graph Theory 
MA015

Graph Algorithms 
MV008

Algebra I 
MA018

Numerical Methods 
PřF:M7130

Computational geometry 
Natural Language Processing Pass at least 1 course of the following list  
PA164

Machine learning and natural language processing 
PV061

Machine Translation 
IV029

Introduction to Transparent Intensional Logic 
Seminar or Project Obtain at least 2 credits by passing subjects of the following list  
PV173

Natural Language Processing Seminar 
PV277

Programming Applications for Social Robots 
PB106

Corpus Linguistic Project I 
PA107

Corpus Tools Project 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Bioinformatics and System Biology
Specialization Bioinformatics and System Biology is intended for students who want to acquire, besides the general knowledge of informatics, the latest knowledge in dynamically developing fields at the border of informatics and biology. By selecting this specialization, the student acquires deep knowledge about the processing, storage, and analysis of biological data or the use of formal methods for analysis and prediction of the behavior of biological systems.
Compulsory subjects of the specialization
IV106 
Bioinformatics seminar 

IV108 
Bioinformatics II 
IV110 
Bioinformatics project I 
IV120 
Continuous and Hybrid Systems 
PA054 
Formal Methods in Systems Biology 
PA183 
Project in Systems Biology 
PB050 
Modelling and Prediction in Systems Biology 
PB172 
Systems Biology Seminar 
PV027 
Optimization 
PV225 
Laboratory of Systems Biology 
Applications Pass at least 1 course of the following list  
PV269

Advanced methods in bioinformatics 
PV270

Biocomputing 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
followup master's program (Czech) with specializations
The study program Visual Informatics prepares students to work with image information and spatial scene models that involve or touch areas such as computer graphics, image processing, visualization, computer vision, virtual and expanded reality, video processing, pattern recognition, humancomputer communication, 3D modeling, animation, graphic design, and machine learning.
The graduate will find application in various fields, such as the development of graphics applications, simulators, computer games, applications for multimedia processing and analysis, visualization of data, virtual and enhanced reality or creation of the professionallevel graphic design. For example, a graduate may be an analyst, graphic designer, application programmer, research or development team leader. The acquired theoretical knowledge and practical skills allow them to thoroughly understand the problems solved and will make it possible in practice to use a wide range of modern technologies  from common mobile devices to dedicated systems with high computing power.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
IV003 
Algorithms and Data Structures II 

MA018 
Numerical Methods 
MV013 
Statistics for Computer Science 
PA103 
Objectoriented Methods for Design of Information Systems 
PA010 
Intermediate Computer Graphics 
PV021 
Neural Networks 
PV182 
HumanComputer Interaction 
PV189 
Mathematics for Computer Graphics 
VV035 
3D Modeling 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
Specialization: Computer Graphics and Visualization
Computer Graphics and Visualization specialization offers a set of courses about basic principles, as well as the latest achievements in computer graphics and data visualization. These are accompanied by courses providing the students with the necessary basic background in informatics. We are particularly focusing on the applicability of the presented topics and their utilization in other disciplines and research areas. Students will learn about basic principles and algorithms, forming the building blocks of final visual outputs. These can be, for example, in a form of realtime rendering or large scenes or visualization design of complex multidimensional datasets. In seminars and projects, students will enrich this knowledge by implementational tasks on selected topics.
Compulsory subjects of the specialization
MA017 
Geometric Algorithms 

PA213 
Advanced Computer Graphics 
PA093 
Computational Geometry Project 
PA157 
Seminar on Computer Graphics Research 
PA166 
Advanced Methods of Digital Image Processing 
PA214 
Visualization II 
PV160 
Laboratory of HumanComputer Interaction 
PV227 
GPU Rendering 
PV251 
Visualization 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Image Processing and Analysis
Image Processing and Analysis specialization provides a comprehensive view of getting and processing image information, starting with simple image editing using point transformations or linear filters, and ending with sophisticated tools such as mathematical morphology or deformable models. Graduates will find their place in the development and deployment of imaging systems in a variety of fields, for example in medicine, biology, meteorological and geographic data processing, biometric applications, etc.
Compulsory subjects of the specialization
MA017 
Geometric Algorithms 

PA093 
Computational Geometry Project 
PA166 
Advanced Methods of Digital Image Processing 
PA170 
Digital Geometry 
PA171 
Integral and Discrete Transforms in Image Processing 
PA172 
Image Acquisition 
PA173 
Mathematical Morphology 
PV187 
Seminar of digital image processing 
PV197 
GPU Programming 
PA228 
Machine Learning in Image Processing 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Computer Game Development
Computer Games Development specialization gives students insight into the multidisciplinary process of digital games development. Students will get acquainted with the principles of game design as well as with modern tools and techniques for the implementation of games and other applications based on game technologies, including the use of augmented and virtual reality. Emphasis is also placed on the visual aspects of game development – from the authoring of 3D models up to the programming of modern graphics cards. In addition to lectures covering theoretical principles, the study also includes several projectoriented seminars that will enable students to gain experience in the area of the game development and expand their professional portfolio. A mandatory part of the studies is also an internship in a game studio lasting 480 hours.
Compulsory subjects of the specialization
PA213 
Advanced Computer Graphics 

PA215 
Game Design I 
PA216 
Game Design II 
PA217 
Artificial Intelligence for Computer Games 
SA300 
Internship  Computer Games 
PV227 
GPU Rendering 
PV255 
Game Development I 
PV266 
Game Development II 
VV036 
3D Character Modeling 
Game Development Pass at least 1 course of the following list  
PA199

Advanced Game Development 
PV283

Games User Research Lab 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Graphic Design
Graphic Design Specialization offers the study of graphic design and related disciplines in cooperation with the Graphic Design and Multimedia Studio (AGD + M). The studio focuses primarily on digital media, which nowadays replaces most of the printed forms. In terms of mastering highquality graphic design, this is an identical problem, but digital media opens up new opportunities in communicating with the consumer. For these media, concurrent informatic education of students is necessary and is developed in the course of this specialization. Students work on topics such as game making, interactive information graphics, creating interactive media applications, generative programming, animation, video, 3D digital modeling and 3D printing, epublishing, webdesign, font creation, and more.
Compulsory subjects of the specialization
PV067 
Typography II 

PV083 
Graphic Design II 
PV085 
Type Design II 
PV257 
Graphic Design and Multimedia Project 
PV259 
Generative Design Programming 
PV268 
Digital Design 
VV051 
Animation 
Gr.Design I Pass at least 1 course of the following list  
PV112

Computer Graphics API 
PV239

Mobile Application Development 
VV036

3D Character Modeling 
Gr.Design II Pass at least 3 courses of the following list  
PV156

Digital Photography 
VV067

Concept and Intermedia 
VV034

Photography  artificial effects 
VV050

Motion Design 
PV110

Basics of Film Narratives 
PV101

Type Design III 
PV251

Visualization 
PV097

Visual Creativity Informatics 
PV113

Production of Audiovisual Artefacts 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
followup master's program (Czech) with specializations
The study program Computer Systems, Communications and Security aims to lead its graduate to an understanding of architectures, principles, design methods and operations of secure computer systems, respecting both hardware and software aspects, including network communications. The graduate will also gain deeper knowledge in of the chose specializations of the programme.
Program graduate will be prepared to design and maintain operations of secure computer systems with respect to both hardware and software aspects, including network communications. Graduate in the specialization Hardware Systems will be prepared to design solutions to practical problems with the use of computer hardware, to creatively adjust hardware systems and to deploy them. Graduate in the specialization Software Systems will be ready to take various roles in the IT departments taking part in the development and operations of information systems and in the use of IT for support of organizations. Graduates of the specialization Information Security will be able to work in organizations developing or providing systems respecting security requirements, but also in advanced management and operations of such systems. Graduate on the specialization Computer Networks and Communications will be able to work as an architect of large networks, manage network operations and related projects, or to work as an expert in applications or security of computer networks.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
IA174 
Fundaments of Cryptography 

MV013 
Statistics for Computer Science 
PA191 
Advanced Computer Networking 
PV079 
Applied Cryptography 
PV260 
Software Quality 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
Math Pass at least 2 courses of the following list  
IV111

Probability in Computer Science 
MA007

Mathematical Logic 
MA010

Graph Theory 
MA012

Statistics II 
MA015

Graph Algorithms 
MA018

Numerical Methods 
Theory of Informatics Pass at least 1 course of the following list  
IA008

Computational Logic 
IA101

Algorithmics for Hard Problems 
IA169

System Verification and Assurance 
IV054

Coding, Cryptography and Cryptographic Protocols 
Hardware Systems Pass at least 2 courses of the following list  
IA158

Real Time Systems 
PA174

Design of Digital Systems II 
PA175

Digital Systems Diagnostics II 
PA176

Architecture of Digital Systems II 
PA190

Digital Signal Processing 
PA192

Secure hardwarebased system design 
PA221

Hardware description languages 
PV191

Seminar in Digital System Design 
PV193

Accelerating Algorithms at Circuit Level 
PV194

External Environments of Digital Systems 
PV198

Onechip Controllers 
PV200

Introduction to hardware description languages 
Specialization: Hardware Systems
Specialization Hardware Systems provides specific knowledge to work with programmable structures extending into parallel and distributed systems, computer networks and cryptography. Teaching emphasizes the balance of courses providing the necessary theoretical basis and courses focusing on practical skills that are involved in the design, implementation, analysis, testing and operation of embedded systems. An integral part of the study is also working on a project with a small team and oriented towards experimental and prototype solutions to interesting problems associated with the solution of practical problems arising from research and development activities of the faculty.
Compulsory subjects of the specialization
IA158 
Real Time Systems 

PB170 
Seminar on Digital System Design 
PB171 
Seminar on Digital System Architecture 
PA175 
Digital Systems Diagnostics II 
PA176 
Architecture of Digital Systems II 
PA190 
Digital Signal Processing 
PV191 
Seminar in Digital System Design 
PV198 
Onechip Controllers 
PV200 
Introduction to hardware description languages 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Software Systems
Specialization Software Systems will lead the graduate to knowledge and skills necessary in all stages of development and changes in extensive software systems, especially information systems. Emphasis is set on knowledge necessary at the design and development of systems with on deployed modern software technologies.
Compulsory subjects of the specialization
IV003 
Algorithms and Data Structures II 

PA017 
Software Engineering II 
PA039 
Supercomputer Architecture and Intensive Computations 
PA103 
Objectoriented Methods for Design of Information Systems 
PA152 
Efficient Use of Database Systems 
PA160 
NetCentric Computing II 
PA165 
Enterprise Applications in Java 
PV217 
Service Oriented Architecture 
PV258 
Software Requirements Engineering 
Programming Obtain at least 3 credits by passing subjects of the following list  
PV179

System Development in C#/.NET 
PV248

Python Seminar 
PV249

Development in Ruby 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Information Security
Specialization Information Security focuses on areas of security in computer systems and networks, cryptography and its applications. The aim is to prepare such a graduate who will be able to work in a variety of roles critical to ensure security of ICTs – specific profiling (e.g., toward cryptography, technological aspects or security management) beyond a common basis of field of study is left to the choice of the student.
Compulsory subjects of the specialization
PV181 
Laboratory of security and applied cryptography 

PV204 
Security Technologies 
PA197 
Secure Network Design 
PA193 
Seminar on secure coding principles and practices 
PV286 
Secure coding principles and practices 
PA018 
Advanced Topics in Information Technology Security 
PA168 
Postgraduate seminar on IT security and cryptography 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Networks and Communication
Computer Networks and Communications specialization focuses on acquiring advanced knowledge of architectures, operation principles, and principles of operation of computer networks. The field is conceived to satisfy both those interested in practically oriented advanced information and knowledge in the field of computer networks and their applications, as well as those interested in deeper acquaintance with the theoretical fundaments of the field and the study of computer networks as a special case of distributed systems. In addition to knowledge of computer networks, the student acquires knowledge of security, principles of working with multimedia data, basic knowledge of parallel systems and necessary theoretical background.
Compulsory subjects of the specialization
PA039 
Supercomputer Architecture and Intensive Computations 

PA053 
Distributed Systems and Middleware 
PA151 
Advanced Computer Networks 
PA160 
NetCentric Computing II 
PV169 
Communication Systems Basics 
PV188 
Principles of Multimedia Processing and Transport 
PV233 
Switching, Routing and Wireless Essentials 
PV234 
Enterprise Networking, Security, and Automation 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
followup master's program (Czech) with specializations
Software systems are in an increasing way supporting most activities of human endeavour, which puts emphasis on the quality of their design, development, testing, deployment and operations. Software engineering integrates skills, techniques and tools for systematic support of these activities, with emphasis on guaranteed quality of the software product. The goal of the study programme is to build the competencies of the students related to software engineering, including their understanding of deeper relations necessary when developing largescale software systems, where each individual design decision critically impacts the quality and vitality of the final system or service. An integral part of the education is the practical training in terms of software development, as well as working within a software team, including experience with teamleading. These skills are necessary for meeting the expectations of the relevant job positions in industry. The practical skills will be acquired mainly within internships in industry, but also when leading projects of bachelor students at the faculty. Given that the degree program is accredited in a professional profile and the content of the curriculum does not include the full scope of compulsory practice, it is assumed that the student enters the degree program in a situation where he completed part of compulsory practice at the bachelor's degree. If this is not the case, he/she is obliged to complete this part of the compulsory practice beyond the scope of the study plan.
The graduates of this study programme are equipped for the position of a senior software developer (in case of the Design and development of software systems) and a deployment (or DevOps) engineer (in case of the Deployment and operations of software systems), including leading roles within software development teams.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
 Fulfil the condition of 18 weeks (in total) of supervised professional internship (at least 12 weeks need to be realized within this master study, while up to 6 weeks of internships can be included from the previous bachelor study).
Compulsory subjects of the program
PA017 
Software Engineering II 

PV157 
Authentication and Access Control 
PV260 
Software Quality 
PA179 
Project Management 
PA053 
Distributed Systems and Middleware 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
SA200 
Internship  Software Engineering 
Programing Obtain at least 12 credits by passing subjects of the following list  
PA165

Enterprise Applications in Java 
PV179

System Development in C#/.NET 
PV168

Seminar in Java programming 
PV178

Introduction to Development in C#/.NET 
PV264

Advanced Programming in C++ 
PV248

Python Seminar 
PV249

Development in Ruby 
PV255

Game Development I 
PV197

GPU Programming 
PV198

Onechip Controllers 
PV239

Mobile Application Development 
PV281

Programming in Rust 
PV288

Python 
Advanced Programing Pass at least 1 course of the following list  
PA165

Enterprise Applications in Java 
PV179

System Development in C#/.NET 
Data Storage Pass at least 1 course of the following list  
PV003

Relational Database System Architecture 
PA152

Efficient Use of Database Systems 
Networking Pass at least 1 course of the following list  
PA159

NetCentric Computing I 
PA191

Advanced Computer Networking 
Specialization: Design and Development of Software Systems
Within the Design and development of software systems specialization, the emphasis is put on the design of highquality software architecture and skills in programming and software development as such (including userinterface design, secure coding principles, data analytics).
Compulsory subjects of the specialization
PA103 
Objectoriented Methods for Design of Information Systems 

PA187 
Project managment and project 
PA036 
Database System Project 
Extended Programing Obtain at least 17 credits by passing subjects of the following list  
PA165

Enterprise Applications in Java 
PA200

Cloud Computing 
PV179

System Development in C#/.NET 
PV168

Seminar in Java programming 
PV178

Introduction to Development in C#/.NET 
PV264

Advanced Programming in C++ 
PV248

Python Seminar 
PV249

Development in Ruby 
PV255

Game Development I 
PV197

GPU Programming 
PV198

Onechip Controllers 
PV239

Mobile Application Development 
PV281

Programming in Rust 
PV288

Python 
Data Analysis Pass at least 1 course of the following list  
PA220

Database systems for data analytics 
PV212

Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning 
Design and Analysis Pass at least 1 course of the following list  
PV167

Seminar on Design and Architecture Patterns 
PV258

Software Requirements Engineering 
Information Security Pass at least 1 course of the following list  
PV286

Secure coding principles and practices 
PA193

Seminar on secure coding principles and practices 
PV276

Seminar on Simulation of Cyber Attacks 
PV017

Information Technology Security 
User Interfaces Pass at least 1 course of the following list  
PV247

Modern Development of User Interfaces 
PV278

Development of Intuitive User Interfaces 
PV182

HumanComputer Interaction 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Deployment and Operations of Software Systems
Within the Deployment and operations of software systems specialization, the emphasis is put on the design of highquality infrastructure for the operation of the software system and the ability to interlink the software development with its deployment and operation (including topics like secure infrastructure design, computer networks, cloud computing, UNIX administration).
Compulsory subjects of the specialization
PA195 
NoSQL Databases 

PA160 
NetCentric Computing II 
PV175 
MS Windows Systems Management I 
PV065 
UNIX  Programming and System Management I 
PV077 
UNIX  Programming and System Management II 
PA200 
Cloud Computing 
Information Security Pass at least 2 courses of the following list  
PA018

Advanced Topics in Information Technology Security 
PA211

Advanced Topics of Cyber Security 
PV276

Seminar on Simulation of Cyber Attacks 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
followup master's program (Czech) with specializations
The study program develops unique competence profile of the student based on the intersection of multiple areas of knowledge that are relevant for managing the development of software systems and services, as well as cybersecurity management. A specific feature is a focus on strategic and operational management related to the targeting, design, implementation, and operation of software systems and services within the context of organizations and different types with a possible focus on their safe operation or IT services. In addition to developing basic theoretical and technological knowledge and practical developmental skills acquired in the bachelor's study, the content of the followup study is extended by other dimensions such as theories and practices of team, project and process management, communication, soft skills and knowledge essential to functioning in economic relations  the basics of marketing, law and others, which especially (but not only) concerns the specialization of service development. The cybersecurity study takes into account aspects of overlapping computer data processing outside of tightly defined system perimeters (e.g. impacting on critical infrastructure), thus enabling a specific multidisciplinary overlap of technical, social and legal aspects in this area.
The graduates find employment in companies and organizations of different sizes and orientation, but they also get the motivation and the possibility of basic preparation for their own innovative business. The strong competitive advantage of the program graduates is the ability to solve complex managementrelated problems of the development of systems and services for which they can use the acquired skills by the study. Their potential is predestined to hold managerial positions, such as the Chief Information Officer (CIO), project manager, and risk manager. Graduates of the cybersecurity management specialization will find application primarily in companies and institutions that need specialists able to work with relevant coordinating institutions and ensure the management of cybersecurity processes. These are positions as a cybersecurity manager and Chief Information Security Officer (CISO).
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
PA017 
Software Engineering II 

PV206 
Communication and Soft Skills 
PV079 
Applied Cryptography 
MV013 
Statistics for Computer Science 
PA152 
Efficient Use of Database Systems 
PA179 
Project Management 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
SA100 
Internship  Management 
Management Pass at least 1 course of the following list  
PA182

Managing in Reality 
PV214

IT Service Management based on ITIL 
PV215

Management by Competencies 
PV237

Strategy and Leadership 
PV271

Risk Management in IT 
PV203

IT Services Management 
PV274

Data Quality Management Seminar 
Specialization: Software Systems Development and Management
Software Systems Development and Managment specialization focuses on software engineering, i.e., to acquire the knowledge and skills needed at all stages of development, management and maintenance of information, and other types of large software systems. The specialization emphasizes the ability to analyse and specify system requirements, system design, and implementation and deployment.
Compulsory subjects of the specialization
IA169 
System Verification and Assurance 

PA053 
Distributed Systems and Middleware 
PA103 
Objectoriented Methods for Design of Information Systems 
PA165 
Enterprise Applications in Java 
PA197 
Secure Network Design 
PV028 
Applied Information Systems 
Programming Pass at least 1 course of the following list  
PA036

Database System Project 
PV179

System Development in C#/.NET 
PV229

Multimedia Similarity Searching in Practice 
PV247

Modern Development of User Interfaces 
PV248

Python Seminar 
PV249

Development in Ruby 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Service Development Management
Services Development Management specialization follows the current large shift from the traditional paradigm of IT design to IT as a service and from productoriented economy to serviceoriented one. Problems and tasks in IT are becoming more complex and the knowledge of IT technology is not sufficient for solving them. A multidisciplinary view is the core of this specialization. Students will gain not only sound IT knowledge (programming, databases, computer security, networks, etc.), but also the skills necessary to understand problems in their complexity (marketing, management, finance or law) as well as necessary communication competencies.
Compulsory subjects of the specialization
PA116 
Domain Understanding and Modeling 

PA194 
Introduction to Service Science 
PA181 
Services  Systems, Modeling and Execution 
PV207 
Business Process Management 
Computer networks Pass at least 1 course of the following list  
PA151

Advanced Computer Networks 
PA159

NetCentric Computing I 
PA191

Advanced Computer Networking 
PA211

Advanced Topics of Cyber Security 
PV210

Cybersecurity in an Organization 
PV177

Laboratory of Advanced Network Technologies 
Economy Pass at least 1 course of the following list  
PV028

Applied Information Systems 
PV241

Enterprise and Financial Management 
Programming Pass at least 1 course of the following list  
PA036

Database System Project 
PA165

Enterprise Applications in Java 
PV179

System Development in C#/.NET 
PV229

Multimedia Similarity Searching in Practice 
PV247

Modern Development of User Interfaces 
PV248

Python Seminar 
PV249

Development in Ruby 
Soft skills Pass at least 1 course of the following list  
ESF:MPV_RKMD

Communication and Managerial Skills training 
ESF:MPV_COMA

Communication and Managerial Skills Training 
ESF:MPP_CEIT

Czech and European Law of Information Technologies 
PV236

Time Management and Effectiveness 
PV209

Person Centered Communication 
IV057

Seminar on Information Society 
PV263

Intercultural Management 
IV064

Information Society 
PA212

Advanced Search Techniques for Large Scale Data Analytics 
Marketing Pass at least 1 course of the following list  
PV216

Marketing Strategy in Service Business 
PV240

Introduction to service marketing 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Specialization: Cybersecurity Managment
Cybersecurity Management specialization takes into account the aspects of computer data processing beyond the welldefined system perimeters (e.g., critical infrastructure impact), reflected in the area of cybersecurity and allowing a specific multidisciplinary overlap of both technical and social and legal aspects of cybersecurity.
Compulsory subjects of the specialization
PrF:BVV14K 
Theory and Method of ICT Law 

IA174 
Fundaments of Cryptography 
PrF:BI301K 
ICT Law II 
PA197 
Secure Network Design 
PV204 
Security Technologies 
PA018 
Advanced Topics in Information Technology Security 
PrF:BVV03K 
Cybercriminality 
IV128 
Online Communication from Social Science Perspective 
Computer networks Pass at least 1 course of the following list  
PA151

Advanced Computer Networks 
PA159

NetCentric Computing I 
PA191

Advanced Computer Networking 
PA211

Advanced Topics of Cyber Security 
PV210

Cybersecurity in an Organization 
PV177

Laboratory of Advanced Network Technologies 
Recommended course of study
Fall 2022 (1. term)

PA017
Software Engineering II 
PV206
Communication and Soft Skills 
PV079
Applied Cryptography 
PrF:BVV14K
Theory and Method of ICT Law 
IA174
Fundaments of Cryptography  Choice: Any course from Computer networks section
Spring 2023 (2. term)
Fall 2023 (3. term)
Spring 2024 (4. term)
 Choice: Any course from Management section

PrF:BVV03K
Cybercrime and Cybersecurity 
IV128
Online Communication from Social Science Perspective 
SDIPR
Diploma Thesis 
SOBHA
Defence of Thesis 
SZMGR
State Exam (MSc degree)
followup master's program (Czech) without specializations supporting Major/Minor study
The aim of this program is to prepare graduates with a range of competencies necessary for the teaching profession. They have both knowledge and skills regarding pupil education, classroom management, and addressing specific learning situations and pupils. The knowledge of individual subjects and the didactic competence ensure a high level of knowledge of the given discipline, which is in accordance to the expected requirements of the secondary schools and the ability of the graduates to mediate the knowledge of the given discipline using a wide range of didactic methods. Graduates are also equipped with the skills and abilities to lead pedagogical communication with students, their parents, colleagues and other subjects (social and communication competencies), educate and motivate pupils, manage classes, participate in school activities and solve specific situations associated with teaching pedagogicalpsychological competencies). In addition, graduates are equipped with diagnostic and special pedagogical competencies that enable them to recognize the individual educational and other needs of students, to prepare individual plans for students, to work with counseling specialists, and to apply a wide range of support measures within an inclusive approach. In addition to pedagogical abilities, this program intends to prepare graduates also for the position of school information system manager and administrator.
Graduates of this master degree study program will primarily act as teachers of relevant subjects at secondary schools (grammar schools and secondary technical schools) with accordance of the accredited fields and their focus. In the case the IT administration study plan, graduates will be able to operate in positions of IT administrators at secondary schools.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Fulfil requirements of IT Teacher and Administrator study option or Major study option.
 Pass all the compulsory and elective courses of the program, selected study option with the highest possible graduation form.
Compulsory subjects of the program
PA159 
NetCentric Computing I 

PV094 
PC Hardware 
PV175 
MS Windows Systems Management I 
PV004 
UNIX 
UA104 
Didactics for Informatics I 
UA105 
Didactics for Informatics II 
UA442 
Exercises in Practical Education I 
UA542 
Exercises in Practical Education II 
UA642 
Exercises in Practical Education III 
VV064 
Academic and Professional Skills in English for IT 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
PřF:XS080 
Special pedagogy 
PřF:XS092 
School management 
PřF:XS093 
Educational activity with gifted learners 
PřF:XS100 
Teacher and school administration 
PřF:XS130 
Personality psychology 
PřF:XS150 
Educational Psychology 
PřF:XS170 
Technology for didactics 
PřF:XS350 
Group dynamic workshop 
Study option: Teacher of Informatics and IT administrator
Study option The Informatics Teacher and Network Administrator prepares students for professional positioning as a Network Administrator at a secondary school in parallel with the pedagogical training necessary to obtain secondary school approbation in Informatics.
Compulsory subjects and other obligations of the study option
UB001 
Assesment of teaching in Informatics 

UA742 
Exercises in Practical Education IV 
UA842 
Exercises in Practical Education V 
PB071 
Principles of lowlevel programming 
PB138 
Modern Markup Languages and Their Applications 
PřF:XS020 
Inspiratorium for teachers 
PřF:XS050 
School pedagogy 
PřF:XS060 
General didactics 
PřF:XS140 
Foundations of Psychology 
PřF:XS090 
Initial teacher training 
PřF:XS220 
Reflective seminar 
Collect at least 36 credits from courses taught at FI with prefixes I or P. 
Recommended course of study
Fall 2022 (1. term)

PA159
NetCentric Computing I 
PV094
PC Hardware 
PřF:XS080
Special pedagogy 
PřF:XS150
Educational Psychology 
UB001
Assesment of teaching in Informatics 
PřF:XS020
Inspiratorium for teachers 
PřF:XS050
School pedagogy 
PřF:XS093
Educational activity with gifted learners 
PřF:XS170
Technology for didactics 
PřF:XS092
School management
Spring 2023 (2. term)

UA104
Didactics for Informatics I 
UA442
Exercises in Practical Education I 
PřF:XS130
Personality psychology 
PřF:XS060
General didactics 
PřF:XS140
Foundations of Psychology 
PřF:XS090
Initial teacher training 
PřF:XS220
Reflective seminar 
VV064
Academic and Professional Skills in English for IT 
PV004
UNIX
Fall 2023 (3. term)
Study option: Minor
This study option leads students in cooperation with the Faculty of Science of Masaryk University to obtain two secondary school approbations.
Compulsory subjects and other obligations of the study option
PA159 
NetCentric Computing I 

PV175 
MS Windows Systems Management I 
PV094 
PC Hardware 
UA104 
Didactics for Informatics I 
UA105 
Didactics for Informatics II 
UA442 
Exercises in Practical Education I 
UA542 
Exercises in Practical Education II 
UA642 
Exercises in Practical Education III 
SZMGR 
State Exam (MSc degree) 
Collect at least 22 credits from courses taught at FI with prefixes I or P. 
Recommended course of study
Fall 2022 (1. term)
Fall 2023 (3. term)
Followup Master's Degree Programs (English)
followup master's program (English) with specializations
 English
 doc. RNDr. Petr Matula, Ph.D.
The study program Visual Informatics prepares students to work with image information and spatial scene models that involve or touch areas such as computer graphics, image processing, visualization, computer vision, virtual and expanded reality, video processing, pattern recognition, humancomputer communication, 3D modeling, animation, graphic design, and machine learning.
The graduate will find application in various fields, such as the development of graphics applications, simulators, computer games, applications for multimedia processing and analysis, visualization of data, virtual and enhanced reality or creation of the professionallevel graphic design. For example, a graduate may be an analyst, graphic designer, application programmer, research or development team leader. The acquired theoretical knowledge and practical skills allow them to thoroughly understand the problems solved and will make it possible in practice to use a wide range of modern technologies  from common mobile devices to dedicated systems with high computing power.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
IV003 
Algorithms and Data Structures II 

MA018 
Numerical Methods 
MV013 
Statistics for Computer Science 
PA103 
Objectoriented Methods for Design of Information Systems 
PA010 
Intermediate Computer Graphics 
PV021 
Neural Networks 
PV182 
HumanComputer Interaction 
PV189 
Mathematics for Computer Graphics 
VV035 
3D Modeling 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
Specialization: Computer Graphics and Visualization
Computer Graphics and Visualization specialization offers a set of courses about basic principles, as well as the latest achievements in computer graphics and data visualization. These are accompanied by courses providing the students with the necessary basic background in informatics. We are particularly focusing on the applicability of the presented topics and their utilization in other disciplines and research areas. Students will learn about basic principles and algorithms, forming the building blocks of final visual outputs. These can be, for example, in a form of realtime rendering or large scenes or visualization design of complex multidimensional datasets. In seminars and projects, students will enrich this knowledge by implementational tasks on selected topics.
Compulsory subjects of the specialization
MA017 
Geometric Algorithms 

PA213 
Advanced Computer Graphics 
PA093 
Computational Geometry Project 
PA157 
Seminar on Computer Graphics Research 
PA166 
Advanced Methods of Digital Image Processing 
PA214 
Visualization II 
PV160 
Laboratory of HumanComputer Interaction 
PV227 
GPU Rendering 
PV251 
Visualization 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Image Processing and Analysis
Image Processing and Analysis specialization provides a comprehensive view of getting and processing image information, starting with simple image editing using point transformations or linear filters, and ending with sophisticated tools such as mathematical morphology or deformable models. Graduates will find their place in the development and deployment of imaging systems in a variety of fields, for example in medicine, biology, meteorological and geographic data processing, biometric applications, etc.
Compulsory subjects of the specialization
MA017 
Geometric Algorithms 

PA093 
Computational Geometry Project 
PA166 
Advanced Methods of Digital Image Processing 
PA170 
Digital Geometry 
PA171 
Integral and Discrete Transforms in Image Processing 
PA172 
Image Acquisition 
PA173 
Mathematical Morphology 
PV187 
Seminar of digital image processing 
PV197 
GPU Programming 
PA228 
Machine Learning in Image Processing 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Computer Game Development
Computer Games Development specialization gives students insight into the multidisciplinary process of digital games development. Students will get acquainted with the principles of game design as well as with modern tools and techniques for the implementation of games and other applications based on game technologies, including the use of augmented and virtual reality. Emphasis is also placed on the visual aspects of game development – from the authoring of 3D models up to the programming of modern graphics cards. In addition to lectures covering theoretical principles, the study also includes several projectoriented seminars that will enable students to gain experience in the area of the game development and expand their professional portfolio. A mandatory part of the studies is also an internship in a game studio lasting 480 hours.
Compulsory subjects of the specialization
PA213 
Advanced Computer Graphics 

PA215 
Game Design I 
PA216 
Game Design II 
PA217 
Artificial Intelligence for Computer Games 
SA300 
Internship  Computer Games 
PV227 
GPU Rendering 
PV255 
Game Development I 
PV266 
Game Development II 
VV036 
3D Character Modeling 
Game Development Pass at least 1 course of the following list  
PA199

Advanced Game Development 
PV283

Games User Research Lab 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
followup master's program (English) with specializations
The study program Computer Systems, Communications and Security aims to lead its graduate to an understanding of architectures, principles, design methods and operations of secure computer systems, respecting both hardware and software aspects, including network communications. The graduate will also gain deeper knowledge in of the chose specializations of the programme.
Program graduate will be prepared to design and maintain operations of secure computer systems with respect to both hardware and software aspects, including network communications. Graduate in the specialization Hardware Systems will be prepared to design solutions to practical problems with the use of computer hardware, to creatively adjust hardware systems and to deploy them. Graduate in the specialization Software Systems will be ready to take various roles in the IT departments taking part in the development and operations of information systems and in the use of IT for support of organizations. Graduates of the specialization Information Security will be able to work in organizations developing or providing systems respecting security requirements, but also in advanced management and operations of such systems. Graduate on the specialization Computer Networks and Communications will be able to work as an architect of large networks, manage network operations and related projects, or to work as an expert in applications or security of computer networks.
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
IA174 
Fundaments of Cryptography 

MV013 
Statistics for Computer Science 
PA191 
Advanced Computer Networking 
PV079 
Applied Cryptography 
PV260 
Software Quality 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
Math Pass at least 2 courses of the following list  
IV111

Probability in Computer Science 
MA007

Mathematical Logic 
MA010

Graph Theory 
MA012

Statistics II 
MA015

Graph Algorithms 
MA018

Numerical Methods 
Theory of Informatics Pass at least 1 course of the following list  
IA008

Computational Logic 
IA101

Algorithmics for Hard Problems 
IA169

System Verification and Assurance 
IV054

Coding, Cryptography and Cryptographic Protocols 
Hardware Systems Pass at least 2 courses of the following list  
IA158

Real Time Systems 
PA174

Design of Digital Systems II 
PA175

Digital Systems Diagnostics II 
PA176

Architecture of Digital Systems II 
PA190

Digital Signal Processing 
PA192

Secure hardwarebased system design 
PA221

Hardware description languages 
PV191

Seminar in Digital System Design 
PV193

Accelerating Algorithms at Circuit Level 
PV194

External Environments of Digital Systems 
PV198

Onechip Controllers 
PV200

Introduction to hardware description languages 
Specialization: Hardware Systems
Specialization Hardware Systems provides specific knowledge to work with programmable structures extending into parallel and distributed systems, computer networks and cryptography. Teaching emphasizes the balance of courses providing the necessary theoretical basis and courses focusing on practical skills that are involved in the design, implementation, analysis, testing and operation of embedded systems. An integral part of the study is also working on a project with a small team and oriented towards experimental and prototype solutions to interesting problems associated with the solution of practical problems arising from research and development activities of the faculty.
Compulsory subjects of the specialization
IA158 
Real Time Systems 

PB170 
Seminar on Digital System Design 
PB171 
Seminar on Digital System Architecture 
PA175 
Digital Systems Diagnostics II 
PA176 
Architecture of Digital Systems II 
PA190 
Digital Signal Processing 
PV191 
Seminar in Digital System Design 
PV198 
Onechip Controllers 
PV200 
Introduction to hardware description languages 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Software Systems
Specialization Software Systems will lead the graduate to knowledge and skills necessary in all stages of development and changes in extensive software systems, especially information systems. Emphasis is set on knowledge necessary at the design and development of systems with on deployed modern software technologies.
Compulsory subjects of the specialization
IV003 
Algorithms and Data Structures II 

PA017 
Software Engineering II 
PA039 
Supercomputer Architecture and Intensive Computations 
PA103 
Objectoriented Methods for Design of Information Systems 
PA152 
Efficient Use of Database Systems 
PA160 
NetCentric Computing II 
PA165 
Enterprise Applications in Java 
PV217 
Service Oriented Architecture 
PV258 
Software Requirements Engineering 
Programming Obtain at least 3 credits by passing subjects of the following list  
PV179

System Development in C#/.NET 
PV248

Python Seminar 
PV249

Development in Ruby 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Information Security
Specialization Information Security focuses on areas of security in computer systems and networks, cryptography and its applications. The aim is to prepare such a graduate who will be able to work in a variety of roles critical to ensure security of ICTs – specific profiling (e.g., toward cryptography, technological aspects or security management) beyond a common basis of field of study is left to the choice of the student.
Compulsory subjects of the specialization
PV181 
Laboratory of security and applied cryptography 

PV204 
Security Technologies 
PA197 
Secure Network Design 
PA193 
Seminar on secure coding principles and practices 
PV286 
Secure coding principles and practices 
PA018 
Advanced Topics in Information Technology Security 
PA168 
Postgraduate seminar on IT security and cryptography 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
followup master's program (English) with specializations
 English
 doc. RNDr. Tomáš Pitner, Ph.D.
The study program develops unique competence profile of the student based on the intersection of multiple areas of knowledge that are relevant for managing the development of software systems and services, as well as cybersecurity management. A specific feature is a focus on strategic and operational management related to the targeting, design, implementation, and operation of software systems and services within the context of organizations and different types with a possible focus on their safe operation or IT services. In addition to developing basic theoretical and technological knowledge and practical developmental skills acquired in the bachelor's study, the content of the followup study is extended by other dimensions such as theories and practices of team, project and process management, communication, soft skills and knowledge essential to functioning in economic relations  the basics of marketing, law and others, which especially (but not only) concerns the specialization of service development. The cybersecurity study takes into account aspects of overlapping computer data processing outside of tightly defined system perimeters (e.g. impacting on critical infrastructure), thus enabling a specific multidisciplinary overlap of technical, social and legal aspects in this area.
The graduates find employment in companies and organizations of different sizes and orientation, but they also get the motivation and the possibility of basic preparation for their own innovative business. The strong competitive advantage of the program graduates is the ability to solve complex managementrelated problems of the development of systems and services for which they can use the acquired skills by the study. Their potential is predestined to hold managerial positions, such as the Chief Information Officer (CIO), project manager, and risk manager. Graduates of the cybersecurity management specialization will find application primarily in companies and institutions that need specialists able to work with relevant coordinating institutions and ensure the management of cybersecurity processes. These are positions as a cybersecurity manager and Chief Information Security Officer (CISO).
Requirements for successful graduation
 Obtain at least 120 credits overall and pass the final state exam.
 Obtain 20 credits from SDIPR subject and successfully defend Master's Thesis. See more details.
 Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form.
 Fulfil requirements of at least one specialization.
Compulsory subjects of the program
PA017 
Software Engineering II 

PV206 
Communication and Soft Skills 
PV079 
Applied Cryptography 
MV013 
Statistics for Computer Science 
PA152 
Efficient Use of Database Systems 
PA179 
Project Management 
SOBHA 
Defence of Thesis 
SZMGR 
State Exam (MSc degree) 
SA100 
Internship  Management 
Management Pass at least 1 course of the following list  
PA182

Managing in Reality 
PV214

IT Service Management based on ITIL 
PV215

Management by Competencies 
PV237

Strategy and Leadership 
PV271

Risk Management in IT 
PV203

IT Services Management 
PV274

Data Quality Management Seminar 
Specialization: Software Systems Development and Management
Software Systems Development and Managment specialization focuses on software engineering, i.e., to acquire the knowledge and skills needed at all stages of development, management and maintenance of information, and other types of large software systems. The specialization emphasizes the ability to analyse and specify system requirements, system design, and implementation and deployment.
Compulsory subjects of the specialization
IA169 
System Verification and Assurance 

PA053 
Distributed Systems and Middleware 
PA103 
Objectoriented Methods for Design of Information Systems 
PA165 
Enterprise Applications in Java 
PA197 
Secure Network Design 
PV028 
Applied Information Systems 
Programming Pass at least 1 course of the following list  
PA036

Database System Project 
PV179

System Development in C#/.NET 
PV229

Multimedia Similarity Searching in Practice 
PV247

Modern Development of User Interfaces 
PV248

Python Seminar 
PV249

Development in Ruby 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Specialization: Service Development Management
Services Development Management specialization follows the current large shift from the traditional paradigm of IT design to IT as a service and from productoriented economy to serviceoriented one. Problems and tasks in IT are becoming more complex and the knowledge of IT technology is not sufficient for solving them. A multidisciplinary view is the core of this specialization. Students will gain not only sound IT knowledge (programming, databases, computer security, networks, etc.), but also the skills necessary to understand problems in their complexity (marketing, management, finance or law) as well as necessary communication competencies.
Compulsory subjects of the specialization
PA116 
Domain Understanding and Modeling 

PA194 
Introduction to Service Science 
PA181 
Services  Systems, Modeling and Execution 
PV207 
Business Process Management 
Computer networks Pass at least 1 course of the following list  
PA151

Advanced Computer Networks 
PA159

NetCentric Computing I 
PA191

Advanced Computer Networking 
PA211

Advanced Topics of Cyber Security 
PV210

Cybersecurity in an Organization 
PV177

Laboratory of Advanced Network Technologies 
Economy Pass at least 1 course of the following list  
PV028

Applied Information Systems 
PV241

Enterprise and Financial Management 
Programming Pass at least 1 course of the following list  
PA036

Database System Project 
PA165

Enterprise Applications in Java 
PV179

System Development in C#/.NET 
PV229

Multimedia Similarity Searching in Practice 
PV247

Modern Development of User Interfaces 
PV248

Python Seminar 
PV249

Development in Ruby 
Soft skills Pass at least 1 course of the following list  
ESF:MPV_RKMD

Communication and Managerial Skills training 
ESF:MPV_COMA

Communication and Managerial Skills Training 
ESF:MPP_CEIT

Czech and European Law of Information Technologies 
PV236

Time Management and Effectiveness 
PV209

Person Centered Communication 
IV057

Seminar on Information Society 
PV263

Intercultural Management 
IV064

Information Society 
PA212

Advanced Search Techniques for Large Scale Data Analytics 
Marketing Pass at least 1 course of the following list  
PV216

Marketing Strategy in Service Business 
PV240

Introduction to service marketing 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Specialization: Cybersecurity Managment
Cybersecurity Management specialization takes into account the aspects of computer data processing beyond the welldefined system perimeters (e.g., critical infrastructure impact), reflected in the area of cybersecurity and allowing a specific multidisciplinary overlap of both technical and social and legal aspects of cybersecurity.
Compulsory subjects of the specialization
BVV14Keng 
Theory of CyberLaw 

IA174 
Fundaments of Cryptography 
PrF:MVV60K 
Cybersecurity Law 
PA197 
Secure Network Design 
PV204 
Security Technologies 
PA018 
Advanced Topics in Information Technology Security 
PrF:SOC022 
European Cyberlaw 
IV128 
Online Communication from Social Science Perspective 
Computer networks Pass at least 1 course of the following list  
PA151

Advanced Computer Networks 
PA159

NetCentric Computing I 
PA191

Advanced Computer Networking 
PA211

Advanced Topics of Cyber Security 
PV210

Cybersecurity in an Organization 
PV177

Laboratory of Advanced Network Technologies 
Recommended course of study
Fall 2022 (1. term)
Spring 2023 (2. term)
Fall 2023 (3. term)
Spring 2024 (4. term)
 Choice: Any course from Management section

PrF:MVV60K
Cybersecurity Law 
IV128
Online Communication from Social Science Perspective 
SDIPR
Diploma Thesis 
SOBHA
Defence of Thesis 
SZMGR
State Exam (MSc degree)
List of courses open at FI (2022/2023)
This list has been built on 20. 5. 2022. Some minor changes may appear during the year, for the current and most uptodate details see IS MU.
MB141 Linear algebra and discrete mathematics
zk 2/2 3 kr., jaro
 doc. RNDr. Martin Čadek, CSc.  Mgr. David Kruml, Ph.D.
 Prerequisities:
! NOW ( MB151 ) && ( ! MB151  ! MB154 ) && ( ! MB101  ! MB104 )
 Goals: Introduction to linear algebra, analytical geometry and elementary number theory.
 Learning outcomes: At the end of this course, students should be able to: understand basic concepts of linear algebra; apply these concepts to iterated linear processes; solve basic problems in analytical geometry; apply elemntary number theory on kryptography.
 Syllabus:
Obsah kurzu Lineární:
1. Geometry in plane. Complex numbers. 2. Systems of linear equations. Gauss elimination. 3. Operation with matrices. Inverse matrix, determinent. 4. Vector spaces, báses, dimension, coordinates. 5. Linear mappings, eigenvalues and eigenvectors. 6. Linear processes. 7. Afinne geometry. 8. Scalar product. 9. Eukleidian geometry. 10. Elementry number theory. 11. Congruences. 12. Application in kryptography.
MB142 Applied math analysis
zk 2/2 3 kr., podzim
 doc. RNDr. Michal Veselý, Ph.D.
 Prerequisities:
! MB152 && ! NOW ( MB152 ) && ! MB102 && ! MB202
High school mathematics  Goals: This is a basic course of mathematical analysis. The content is differential and integral calculus and infinite series. Students will understand practical methods and will be able to apply these methods to concrete problems. The course places more emphasis on examples.
 Learning outcomes:
At the end of the course students will be able to:
work practically with the derivative and (indefinite and definite) integral;
analyse the behaviour of functions;
understand the use of infinite number series and power series;
understand selected applications of the calculus;
apply the methods of the calculus to concrete problems.  Syllabus:
Continuous functions and limits
Derivatives of functions with applications
Indefinite integrals
Riemann integral and its applications
Series
MB143 Design and analysis of statistical experiments
zk 2/2 3 kr., jaro
 doc. Mgr. David Kraus, Ph.D.
 Prerequisities:
MB141  MB142  MB101  MB201  MB102  MB202  MB151  MB152
 Goals: The course presents principles and methods of statistical analysis, and explains what types of data are suitable for answering questions of interest.
 Learning outcomes:
After the course the students:
 are able to formulate questions of interest in terms of statistical inference (parameter estimation or hypothesis test within a suitable model);
 are able to choose a suitable model for basic types of data, choose a suitable method of inference to answer most common questions, implement the method in the statistical software R, and correctly interpret the results;
 are able to judge which questions and with what accuracy/certainty can be answered based on available data, or suggest what data should be collected in order to answer given questions with a desired level of accuracy/certainty.  Syllabus:
Basic principles of Probability.
Random variables, their characteristics and mutual relationships.
Properties of functions of random variables.
Data as realisations of random variables.
Descriptive statistics and the choice of a suitable model.
Point and interval estimation: the framework and most common methods.
Hypotheses testing: the framework and most common methods.
Linear regression, Analysis of variance, Analysis of covariance.
Methods of data collection, their purpose, scope and limitations.
Design of experiment.
MB151 Linear models
zk 2/2 3 kr., jaro
 doc. Mgr. Ondřej Klíma, Ph.D.
 Prerequisities:
! MB101 && ! MB201
 Goals: Introduction to linear algebra and analytical geometry.
 Learning outcomes: At the end of this course, students should be able to: understand basic concepts of linear algebra; apply these concepts to iterated linear processes; solve basic problems in analytical geometry.
 Syllabus:
The course is the first part of the four semester block of Mathematics. In the entire course, the fundamentals
of general algebra and number theory, linear algebra, mathematical analysis, numerical methods, combinatorics, as well as probability and statistics are presented. Content of the course Linear models:
1. Introduction (3 weeks)  motivating examples, real and complex numbers, roots of real polynomials, matrix multiplication, recurrence relations (incl. recurrence in combinatorics), geometry in two dimensions.
2. Vector spaces (4 weeks)  systems of linear equalities, matrix calculus (determinant and inverse matrix), vector spaces (formal definition and examples), linear independence, basis, coordinates, scalar product, length of vector, orthogonality, explicit formulas for recurrence relations.
3. Linear mappings (2 weeks)  representation of linear mappings, eigenvalues and eigenvectors; linear transformations in three dimensions, iterated linear processes (population models and discrete Markov chains).
4. Analytical geometry (4 weeks)  affine and Euclidean spaces (line, plane descriptions, angle, length, volume); systems of linear (in)equalities  linear programming problem; elementary classification of quadrics.
MB152 Differential and Integral Calculus
zk 2/2 3 kr., podzim
 doc. Mgr. Petr Hasil, Ph.D.
 Prerequisities:
( ! MB202 && ! MB102 )
High school mathematics  Goals: This is a basic course of the mathematical analysis. The content is the differential and integral calculus and the theory of infinite series. Students will understand theoretical and practical methods and will be able to apply these methods to concrete problems. The emphasis on theory and examples is balanced in the course.
 Learning outcomes:
At the end of the course students will be able to:
work both practically and theoretically with the derivative and (indefinite and definite) integral;
analyse the behaviour of functions of one real variable.
understand the theory and use of infinite number series and power series;
understand the selected applications of the calculus;
apply the methods of the calculus to concrete problems.  Syllabus:
Continuous functions and limits
Derivative and its applications
Elementary functions
Indefinite integral
Riemann integral and its applications (including an introduction to basic differential equations)
Introduction to differential (and integral) calculus of functions of several variables
Infinite series
MB153 Statistics I
zk 2/2 3 kr., jaro
 doc. Mgr. Jan Koláček, Ph.D.
 Prerequisities:
( MB151  MB101  MB201  MB152  MB102  MB202 ) && ( ! MB103 && ! MB203 && ! MV011 )
Prerequisites: calculus in one and several variables, basics of linear algebra.  Goals: Introductory course to educate students in descriptive statistics, theory of probability, random values and probabilistic distributions, including the theory of hypothesis testing.
 Learning outcomes: Upon completing this course, students will be able to perform basic computer aided statistical data set analysis in R language, resulting in tables, graphs and numerical characteristics; will understand basic probability concepts; will be able to solve probability tasks related to explained theory (in some cases using statistical software); will be able to generate realizations of selected types random variables using statistical software; has basic knowledge of statistical hypothesis testing, will be able carry out tests in statistical software and interpret the results.
 Syllabus:
Introduction to the probability theory.
Random variables and vectors. Probability distribution and distribution function.
Discrete and continuous random variables and vectors. Typical distribution laws. Simultaneous and marginal distributions.
Stochastic independence of random variables and vectors. The sequence of independent trials.
Quantiles, expectation, variance, covariance, correlation coeficient and their properties.
Weak law of large number and central limit theorem.
Data files, empirical characteristics and graphs, numerical characteristics. Descriptive statistics in R language.
Random sample, point and interval estimators.
Basics of testing hypothesis. Testing hypothesis in R language.
Regression analysis in R language.
MB154 Discrete mathematics
zk 2/2 3 kr., podzim
 prof. RNDr. Jan Slovák, DrSc.  doc. Lukáš Vokřínek, PhD.
 Prerequisities:
! MB104 && ! MB204 && ( MB101  MB201  MB151  MB102  MB202  MB152 )
High school mathematics. Elementary knowledge of algebraic and combinatorial tasks.  Goals: Tho goal of this course is to introduce the basics of theory of numbers with its applications to cryptography, and also the basics of coding and more advanced combinatorial methods.
 Learning outcomes: At the end of this course, students should be able to: understand and use methods of number theory to solve simple tasks; understand approximately how results of number theory are applied in cryptography: understand basic computational context; model and solve simple combinatorial problems.
 Syllabus:
Number theory:
divisiblity (gcd, extended Euclid algorithm, Bezout); numerics of big numbers (gcd, modular exponential); prime numbers (properties, basic theorems of arithmetics, factorization, prime number testing (RabinMiller, Mersenneho prime numbers); congruences (basic properties, small Fermat theorem; Euler theorem; linear congruences; binomial congruences a primitiv roots; discrete logarithm;
Number theory applications:
short introduction to asymetric cryptography (RSA, DH, ElGamal, DSA, ECC); basic coding theory (linear and polynomial codes);
Combinatorics:
reminder of basics of combinatorics; generalized binomial theorem; combinatorial identities; Catalan numbers; formal power series; (ordinary) generating functions; exponential generating functions; probabilistic generating functions; solving combinatorial problems with the help of generating functions; solving basic reccurences (Fibonacci).
MA002 Calculus
zk 2/2 3 kr., podzim
 doc. Mgr. Peter Šepitka, Ph.D.
 Prerequisities: Basic knowledge from the calculus and multivariable calculus
 Goals: This course extends the basic knowledge from mathematical analysis. It is devoted to the basic study of systems of linear differential equations, line integrals, the theory of complex functions of complex variable, and calculus of variations.
 Learning outcomes: At the end of the course students should be able to: to define and interpret the basic notions used in the fields mentioned above; to formulate relevant mathematical theorems and statements and to explain methods of their proofs; to use effective techniques utilized in these subject areas; to apply acquired pieces of knowledge for the solution of specific problems; to analyse selected problems from the topics of the course.
 Syllabus:
Systems of linear differential equations.
Line integral.
Analysis in complex domain.
Calculus of variations.
MA007 Mathematical Logic
zk 2/1 4 kr., podzim
 prof. RNDr. Antonín Kučera, Ph.D.
 Prerequisities:
MB005  MB101  MB201  PřF:M1120  PřF:M1125
Students should have passed the course MB005 Foundations of mathematics or the course MB101 Mathematics I. A recommended course is MB008 Algebra I.  Goals: The course covers basic results about propositional and first order logic, including Gödel's completeness and incompleteness theorems.
 Learning outcomes:
At the end of this course, students should be able to:
understand the difference between formal notions and notions defined at a metalevel;
understand the difference between validity and provability;
understand the syntax and semantics of firstorder logic;
understand and appreciate the fundamental ideas in the proofs of Gödel's completeness and incompleteness theorems.  Syllabus:
Propositional calculus: propositional formulas, truth, provability,
completeness.
Firstorder logic: syntax, semantics.
A deductive system for firstorder logic. Provability, correctness.
Completeness theorem: theories, models, Gödel's completeness theorem
Basic model theory, LöwenheimSkolem theorem
Gödel's incompleteness theorem.
MA010 Graph Theory
zk 2/1 3 kr., podzim
 prof. RNDr. Daniel Kráľ, Ph.D., DSc.
 Prerequisities:
! PřF:M5140 &&! NOW ( PřF:M5140 )
Discrete mathematics. IB000 (or equivalent from other schools) is recommended.  Goals: This is a standard introductory course in graph theory, assuming no prior knowledge of graphs. The course aims to present basic graph theory concepts and statements with a particular focus on those relevant in algorithms and computer science in general. Selected advanced graph theory topics will also be covered. Although the content of this course is primarily targeted at computer science students, it should be accessible to all students.
 Learning outcomes: At the end of the course, students shall understand basic concenpts in graph theory; be able to reproduce the proofs of some fundamental statements in graph theory; be able to solve unseen simple graph theory problems; and be ready to apply their knowledge particularly in computer science.
 Syllabus:
Basic graph theory notions: graphs, subgraph, graph isomorphism, vertex degree, paths, connected components, directed graphs.
Basic graph theory notions: graphs, subgraph, graph isomorphism, vertex degree, paths, cycles, connected components, directed graphs.
Trees, Hamilton cycles, Dirac’s and Ore’s conditions.
Planar graphs, duality of planar graphs, Euler's formula and its applications.
Graph coloring, Five Color Theorem, Brooks’ Theorem, Vizing’s Theorem.
Interval graphs, chordal graphs, and their chromatic properties.
Vertex and edge connectivity.
Matchings in graphs, Hall’s Theorem.
Ramsey's Theorem.
Selected advanced topics (to be chosen from): Graph minors, graph embeddings on surfaces, planarity testing, list coloring, Tutte’s Theorem, Cayley’s formula.
MA012 Statistics II
zk 2/2 3 kr., podzim
 Mgr. Ondřej Pokora, Ph.D.
 Prerequisities: Prerequisites: calculus, basics of linear algebra, probability and statistics including an experience with statistical language R (from course MB153 Statistics I).
 Goals: The course introduces students to advanced methods of mathematical statistics – explains the algorithms, computational procedures, conditions, interpretation of results and practical use of these methods for the analysis of datasets in statistical software R. After completing the course, the student will understand advanced statistical methods and inferential principles (estimations, hypothesis testing). The student will be able to use this methods in analyzing datasets and will be able to statistically interpret the achieved results.
 Learning outcomes:
After completing the course the student will be able to:
 explain the principles and algorithms of advanced methods of mathematical statistics;
 perform a statistical analysis of the real dataset in the software R;
 interpret the results obtained by the statistical analysis.  Syllabus:
Analysis of variance (ANOVA).
Nonparametric tests – rank tests.
Goodnessoffit tests.
Correlation analysis, correlation coefficients.
Multiple regression.
Regression diagnostics.
Autocorrelation and multicollinearity.
Principal component Analysis (PCA).
Logistic regression and other generalized linear models (GLM).
Contingency tables and independence testing.
Bootstrapping.
MA015 Graph Algorithms
zk 2/1 3 kr., podzim
 doc. Mgr. Jan Obdržálek, PhD.
 Prerequisities:
MB005 ( MB101 && MB102 )( MB201 && MB102 )( MB101 && MB202 )( MB201 && MB202 )( PřF:M1120 ) PROGRAM ( N  IN ) PROGRAM ( N  AP )
Knowledge of basic graph algorithms and datastructures. Specifically, students should already understand the following datastructures and algorithms: Graphs searching: DFS, BFS. Network flows: FordFulkerson. Minimum spanning trees: at least one of Boruvka, Jarnik (Prim), Kruskal. Shortest paths: BellmanFord, Dijkstra. Datastructures: priority queues, heaps (incl. Fibonacci), disjoint set (unionfind).  Goals: The course surveys important graph algorithms beyond those typically covered in basic algorithms and data structures courses. Chosen algorithms span most of the important application areas of graphs algorithms.
 Learning outcomes:
At the end of the course students will:
 know and understand efficient algorithms for various graph problems, including: minimum spanning trees, network flows, (globally) minimum cuts, matchings (including the assignment problem);
 be able to prove correctness and complexity of these algorithms;
 be able to use dynamic programming to solve problems on treelike graphs;
 learn a range of techniques useful for designing efficient algorithms and deriving their complexity.  Syllabus:
Minimum Spanning Trees.
Quick overview of basic algorithms (Kruskal, Jarník [Prim], Borůvka) and their modifications. Advanced algorithms: FredmanTarjan, Gabow et al. Randomized algorithms: KargerKleinTarjan. Arborescenses of directed graphs, Edmond's branching algorithm.
Flows in Networks. Revision  FordFulkerson. EdmondsKarp, Dinic's algorithm (and its variants), MPM (three Indians) algorithm. Modifications for restricted networks.
Minimum Cuts in Undirected Graphs. All pairs flows/cuts: GomoryHu trees. Global minimum cut: node identification algorithm (NagamochiIbaraki), random algorithms (Karger, KargerStein)
Matchings in General Graphs. Basic algorithm using augmenting paths. Perfect matchings: Edmond's blossom algorithm. Maximum matchings. Mincost perfect matching: Hungarian algorithm.
Dynamic Algorithms for Hard Problems. Dynamic programming on trees and circulararc graphs. Treewidth; dynamic programming on treedecompositions.
Graph Isomorphism. Colour refinement. Individualisationrefinement algorithms. Tractable classes of graphs.
MA017 Geometric Algorithms
zk 2/0 2 kr., podzim
 doc. John Denis Bourke, PhD  doc. RNDr. Martin Čadek, CSc.
 Prerequisities: Basic course on algorithms, high school geometry.
 Goals: The aim of the course is to introduce the principles of basic algorithms in computational geometry. This course can be followed by the PA093 Computational Geometry Project where the students are implemented selected algorithms in practice.
 Learning outcomes: Students will gain knowledge about stateoftheart algorithmic methods in this field, along with their complexity and underlying data and searching structures.
 Syllabus: 1. Algorithms for construction of convex hulls in twodimensional space 2. Line segment intersections 3. Triangulations 4. Linear programming in twodimensional space 5. Range searching (kdtrees, range trees) 6. Point localization 7. Voronoi diagrams 8. Duality and arrangements 9. Delaunay triangulation 10. Convex hulls in in threedimensional space
MA018 Numerical Methods
zk 2/2 3 kr., podzim
 Mgr. Jiří Zelinka, Dr.
 Prerequisities: Differential and integral calculus of functions of one and more variables. Basic knowledge of linear algebra, theory of matrices and solving systems of linear equations. Basics of programing.
 Goals: This course provides explanation of numerical mathematics as the separate scientific discipline. The emphasis is given to the algorithmization and computer implementation. Examples with graphical outputs help to explain even some difficult parts.
 Learning outcomes: At the end of course students should be able to apply numerical methods for solving practical problems and use these methods in other disciplines.
 Syllabus:
1. Error analysis: absolute and relative error, representation of numbers, error propagation
2. Iterative methods for solving of nonlinear equations: general iterative method, order of the convergence, Newton method and its modifications
3. Direct methods for solving systems of linear equations: methods based on Gaussian elimination, methods for special matrices
4. Iterative methods for solving of systems of linear equations: general construction of iterative methods, Jacobi method, GaussSeidel method
5. Solving of systems of nonlinear equations: Newton method
6. Interpolation and approximation: polynomial and piecewise polynomial interpolation, curve approximations, subdivision schemes, least squares method
7. Numerical differentiation: differentiation schemes
8. Numerical integration: methods based on interpolation, Monte Carlo integration
MV008 Algebra I
zk 2/2 3 kr., podzim
 doc. Mgr. Michal Kunc, Ph.D.
 Prerequisities:
( MB005  MB101  MB201  MB151 ) && ! MB008
 Goals: The aim of the course is to become familiar with basic algebraic terminology, demonstrated on monoids, groups and rings, and with its usage for instance in modular arithmetics or for calculations with permutations and numbers.
 Learning outcomes: After passing the course, students will be able to: use the basic notions of the theory of monoids, groups and rings; define and understand basic properties of these structures; verify simple algebraic statements; apply theoretical results to algorithmic calculations with numbers, mappings and polynomials.
 Syllabus:
Semigroups: monoids, subsemigroups and submonoids, homomorphisms and isomorphisms, Cayley's representation, transition monoids of automata, direct products of semigroups, invertible elements.
Groups: basic properties, subgroups, homomorphisms and isomorphisms, cyclic groups, Cayley's representation, direct products of groups, cosets of a subgroup, Lagrange's theorem, normal subgroups, quotient groups.
Polynomials: polynomials over complex, real, rational and integer numbers, polynomials over residue classes, divisibility, irreducible polynomials, roots, minimal polynomials of numbers.
Rings: basic properties, subrings, homomorphisms and isomorphisms, direct products of rings, integral domains, fields, fields of fractions, divisibility, polynomials over a field, ideals, quotient rings, field extensions, finite fields.
MV013 Statistics for Computer Science
zk 2/2 3 kr., jaro
 RNDr. Radim Navrátil, Ph.D.
 Prerequisities:
Basic knowledge of mathematical analysis: functions, limits of sequences and functions, derivatives and integrals of real and multidimensional functions.
Basic knowledge of linear algebra: matrices and determinants, eigenvalues and eigenvectors.
Basic knowledge of probability theory: probability, random variables and vectors, limit theorems.  Goals: The main goal of the course is to become familiar with some basic principles of statistics, with writing about numbers (presenting data using basic characteristics and statistical graphics), some basic principles of likelihood and statistical inference; to understand basic probabilistic and statistical models; to understand and explain basic principles of parametric statistical inference for continuous and categorical data; to implement these techniques to R language; to be able to apply them to real data.
 Learning outcomes:
Student will be able:
 to understand principles of likelihood and statistical inference for continuous and discrete data;
 to select suitable probabilistic and statistical model for continous and discrete data;
 to use suitable basic characteristics and statistical graphics for continous and discrete data;
 to build up and explain suitable statistical test for continuous and discrete data;
 to apply statistical inference on real continuous and discrete data;
 to apply simple linear regression model including ANOVA on real continuous data;
 to implement statistical methods of continuous and discrete data to R.  Syllabus:
What is statistics? Motivation and examples.
Exploratory data analysis
Revision of probability theory
Parametric models  methods for parameter estimation
Confidence intervals and hypothesis testing
Testing hypotheses about onesample
Testing hypotheses about twosamples
ANOVA
Testing for independence
Nonparametric tests
Linear regression models
IB000 Mathematical Foundations of Computer Science
zk 2/2 4 kr., podzim
 prof. RNDr. Petr Hliněný, Ph.D.
 Goals: This course is focused on understanding basic mathematical concepts necessary for study of computer science. This is essential for building up a set of basic concepts and formalisms needed for other theoretical courses in computer science. At the end of this course the successful students should: know the basic mathematical notions; understand the logical structure of mathematical statements and mathematical proofs, specially mathematical induction; know discrete mathematical structures such as finite sets, relations, functions, and graph; be able to precisely formulate their claims, algorithms, and relevant proofs; and apply acquired knowledge in other CS courses as well as in practice later on.
 Learning outcomes: After finishing the course the student will be able to: understand the logical structure of mathematical statements and mathematical proofs, deal with and explain basic structures of discrete mathematics, precisely formulate their claims, algorithms, and relevant proofs.
 Syllabus:
The course focuses on understanding basic mathematical tools:
Basic formalisms  statements, proofs, and propositional logic.
Sets, relations, and functions.
Proof techniques, mathematical induction.
Recursion, structural induction.
Binary relations, closure, transitivity.
Equivalence and partial orders.
Composition of relations and functions.
Basics of graphs, isomorphism, connectivity, trees.
Graph distance, spanning trees. Directed graphs.
Proof techniques for algorithms.
Infinite sets and the halting problem.
IB002 Algorithms and data structures I
zk 2/2 4 kr., jaro
 prof. RNDr. Ivana Černá, CSc.
 Prerequisities:
IB001  IB111  IB999
The students should comprehend the basic notions on the level of IB111 Introduction to Programming and IB000 Mathematical Foundations of Computer Science Students should be able to: understand and apply basic constructs of programming languages (e.g., conditions, loops, functions, basic data types) in Python, know principles of recursion, and several basic algorithms. Students should know the basic mathematical notions; understand the logical structure of mathematical statements and mathematical proofs, specially mathematical induction; know discrete mathematical structures such as finite sets, relations, functions, and graph including their applications in informatics.  Goals: The course presents basic techniques of the analysis of algorithms, data structures, and operations. Students should correctly apply the basic data structures and algorithms as well as apply the algorithm design and analysis techniques when designing new algorithms. Students implement their algorithms in programming language Python.
 Learning outcomes:
After enrolling the course students are able to:
 actively use and modify basic sorting algorithms and graph algorithms,
 actively used basic techniques for designing algorithms (divide et impera, recursion) and design simple algorithms,
 actively used and modify basic static and dynamic data structures,
 employ time complexity and correctness of algorithms,
 analyze time complexity and prove the correctness of simple iterative and recursive algorithms,
 implement algorithms in the selected programming language (Python).  Syllabus:
Basic analysis of algorithms:
The correctness of algorithms, input and output conditions, partial correctness, convergence, verification.
Length of computation, algorithm complexity, problem complexity. Asymptotical analysis of time and space complexity, growth of functions.
Algorithm design techniques. Divide et impera and recursive algorithms.
Fundamental data structures: lists, queues. Representation of sets, hash tables. Binary heaps. Binary search trees, balanced trees (B trees, Redblack trees).
Sorting algorithms: quicksort, mergesort, heapsort, lower bound for the time complexity of sorting.
Graphs and their representation. Graph search. Depthfirst traversal, topological sort, strongly connected components. Breadthfirst traversal, bipartite graphs. Shortest paths, algorithm BellmanFord, Dijkstra's algorithm.
IB005 Formal Languages and Automata
zk 2/2 4 kr., jaro
 prof. RNDr. Mojmír Křetínský, CSc.
 Prerequisities:
IB000 && ! IB102
Knowlegde corresponding to the courses IB000 Mathematical Foundations of Computer Science  Goals: Students should be able to understand and explain the rich heritage of models and abstractions that have arisen over the years, and to develop the students' capacity to form abstractions of their own and reason in terms of them.
 Learning outcomes:
At the end of the course students should be able to:
Demonstrate an indepth understanding of theories, concepts and techniques in automata and their link to computation.
Develop abstract machines that demonstrate the properties of physical/SW systems and be able to specify the possible inputs, processes and outputs of these machines. Analyze the computational strengths and weaknesses of these machines.
Understand the concept of computability by manipulating these machines in order to demonstrate the properties of computational processes.
Practice techniques of program design and development by using abstract machines. Apply automata concepts and techniques in designing systems that address real world problems  Syllabus:
Languages and grammars. Chomsky hierarchy.
Finite automata and regular grammars.
Properties of regular languages. Applications.
Contextfree grammars and pushdown automata.
Properties of contextfree languages.
Turing machines (TM). Computable languages and functions, LBA. Properties of recursive and recursive enumerable languages.
Undecidability, halting problem for TM, Reduction, Post Correspondece Problem, undecidable problems from language theory.
IB015 NonImperative Programming
zk 2/1 4 kr., podzim
 prof. RNDr. Jiří Barnat, Ph.D.
 Prerequisities: There are no special prerequisities apart from the basic math skills (on the secondaryschool level), and certain aptitude for abstract reasoning.
 Goals: On successful completion of the course, students will understand functional and logic programming paradigms. Programming languages enforcing declarative way of description of an algorithm bring on programming habits that the students will be able to use in practice later on when implementing large applications using even imperative languages.
 Learning outcomes: After graduation students will:  understand fundaments of functional programming,  be able to decompose computational problems to individual functions and apply this ability for design and implementation of programs even in imperative programming languages,  have basic knowledge of Haskell programming language  be able to design and implement recursive functions,  be able to work with recursively defined data structures.
 Syllabus:
Functional computational paradigm and Haskell
Functions in programming;
Lists, Types and Recursion
Functions of higher rank, Lambda functions
Accumulators, Type definitions, Input/Output
Reduction strategy, Infinite lists
Relation of recursion and induction, Recursive data types
Time complexity of computation, Type classes, Modules
Functional solutions od some problems
Logical computational paradigm and Prolog
Nonimperative programming in Prologu
Lists, Arithmetics, Tail rekursion in Prologu
Cuts, InputOutput, All solutions
An Introduction to Constraint Solving Programming
IB016 Seminar on Functional Programming
z 1/1 2 kr., jaro
 Bc. Vladimír Chlup  Mgr. Adam Matoušek  RNDr. Vladimír Štill, Ph.D.
 Prerequisities:
IB015
Prerequisities for enrolling in the course are to be familiar with Haskell in the scope of the IB015 NonImperative Programming course and to have a positive attitude towards functional programming.  Goals: Students will significantly extend their knowledge of functional programming. At the end of the course, they should be able to solve nontrivial programming problems using Haskell and be familiar with practical use of this functional language.
 Learning outcomes:
After finishing the course, the student will be able to:
— write a Haskell program with approximatelly 100 to 200 lines;
— perform analysis and functional decompisition of given problem;
— use supportive tools for Haskell developers such as the Cabal package manager, the Hackage package repository, the HLint linter, and the QuickCheck testing framework;
— describe theoretical functional concepts;
— have an idea about some more advanced functional techniques used in practice.  Syllabus:
Advanced syntax, modules, custom type classes, advanced data structures.
Package system (Hackage/Stackage), support tools (Cabal, HLint, Haddock).
Functors, applicative functors, monads.
Automatic generation of tests according to program specification (QuickCheck).
Input and output in Haskell, processing errors and exceptions (Maybe, Either, exceptions, error states).
Semigroups, monoids, the Foldable and Traversable classes.
Evaluation strategies (laziness vs. strictness).
Contextaware traversal of data structures (Zippers, Lens).
Monadic parsing (Parsec).
Monads for shared writing, shared reading and keeping the state (Writer, Reader, State).
Monad transformers (MaybeT, ErrorT).
Processing strings and other useful GHC extensions.
Haskell in real world projects.
IB030 Introduction to Computational Linguistics
zk 2/0 2 kr., jaro
 doc. RNDr. Aleš Horák, Ph.D.
 Goals: In this course the main principles of natural language processing are presented. The algorithmic description of the main language analysis levels will be discussed  morphology, syntax, semantics and pragmatics. Also the resources of natural language data, corpora, will be presented. The role of knowledge representation, inference and relations to artificial intelligence will be touched as well.
 Learning outcomes:
Students will be able to:
 identify and summarize the main phases of computer natural language analysis;
 describe principles of algorithms used for speech analysis;
 explain the main approaches to analysis at the morphological and syntactic level of language;
 provide an overview of main language resources, their formats and processing;
 understand approaches to computational semantics and its applications.  Syllabus:
Introduction to Computational Linguistics (Natural Language Processing, NLP).
Levels of description: phonetics and phonology, morphology, syntax, semantics and pragmatics.
Representation of morphological and syntactic structures.
Analysis and synthesis: speech, morphological, syntactic, semantic.
Knowledge representation forms with regard to lexical units.
Language understanding: sentence meaning representation, logical inference.
IB031 Introduction to Machine Learning
zk 2/2 3 kr., jaro
 doc. RNDr. Tomáš Brázdil, Ph.D.  doc. RNDr. Lubomír Popelínský, Ph.D.
 Prerequisities: Recommended courses are MB102 a MB103.
 Goals: By the end of the course, students should know basic methods of machine learning and understand their basic theoretical properties, implementation details, and key practical applications. Also, students should understand the relationship among machine learning and other subareas of mathematics and computer science such as statistics, logic, artificial intelligence and optimization.
 Learning outcomes:
By the end of the course, students
 will know basic methods of machine learning;
 will understand their basic theoretical properties, implementation details, and key practical applications;
 will understand the relationship among machine learning and other subareas of mathematics and computer science such as statistics, logic, artificial intelligence and optimization;
 will be able to implement and validate a simple machine learning method.  Syllabus:
Basic machine learning: classification and regression, clustering,
(un)supervised learning, simple examples
Decision trees: learning of decision trees and rules
Logic and machine learning: specialization and generalization, logical entailment
Evaluation: training and test sets, overfitting, crossvalidation, confusion matrix, learning curve, ROC curve; sampling, normalisation
Probabilistic models: Bayes rule, MAP, MLE, naive Bayes; introduction to Bayes networks
Linear regression (classification): least squares, relationship wih MLE, regression trees
Kernel methods: SVM, kernel transformation, kernel trick, kernel SVM
Neural networks: multilayer perceptron, backpropagation, nonlinear regression, bias vs variance, regularization
Lazy learning: nearest neighbor method; Clustering: kmeans, hierarchical clustering, EM
Practical machine learning: Data preprocessing: attribute selection and construction, sampling. Ensemble methods. Bagging. Boosting. Tools for machine learning.
Advanced methods: Inductive logic programming, deep learning.
IB047 Introduction to Corpus Linguistics and Computer Lexicography
zk 2/0 2 kr., jaro
 doc. Mgr. Pavel Rychlý, Ph.D.
 Goals: A basic introduction to the field of corpus linguistics and computational lexicography. Students will study types of corpora, corpus building and usage, especially in the sake of dictionaries building.
 Learning outcomes: Student will be able to: choose the right korpus type for specific purpose; interpret all layers of corpus annotation; use statistical methods on text corpora; design the structure of a dictionary; use free tools for dictionary writing.
 Syllabus:
Information technologies and language (text) corpora.
Beginning of corpus linguistics, purpose of corpora.
Corpus data, corpus types and their standardization, SGML, XML, TEI, CES. Annotated corpora, tagging on various levels: structural tagging, grammatical tagging  POS, lemmata, word forms. Syntactic tagging, treebanks, skeleton analysis. Parallel corpora, alignment. Tools for automatic and semiautomatic annotation, disambiguation.
Building corpora, maintenance. Corpus tools: corpus manager. Concordance programmes. Queries, regular expressions and their use. Statistical programmes, absolute and relative frequencies, MI and Tscore. Work with corpus attributes and tags.
Working with corpora  CNC, SUSANNE, Prague Dependency Treebank
Words, constructions, collocations.
Computational lexicography, lexicology.
Descripton of meanings (semantic features).
Types of computer dictionaries. Lexicography standards.
Data for dictionary building  corpora.
Lexicography Software tools. Lemmatizers.
IB053 Efficient Programming Methods
k 1/1 2 kr., jaro
 Mgr. Petr Steinmetz
 Prerequisities: Knowledge of programming at some of languages C, C++, Pascal, Delphi, Java, C#; knowledge of object oriented programming; basic awareness of machine code
 Goals: Purpose of this lecture is to forward knowledge and experiences needed to development of software. A term "efficient programming" is here meant in two sense. Partly as work expended on creation of program that is to be efficient as possible and partly as program self whose implementation is also to be efficient as possible, i.e. so that computer while performs program makes minimum overhead (noneffective activity). Subject of lecture is not algorithm design self in term of complexity.
 Learning outcomes: At the end of course student gains knowledge and routine leading to more efficient creation of software including more efficient implementation od algorithm.
 Syllabus:
Work efficiency in algorithm design.
Cutback of error rate in program creation.
Cutback of time needed to elimination of mistakes.
Usage of earlier written parts of programs.
Independence of program from later changes.
Portability of program to other environment.
Program efficiency.
Data access mechanism.
Implementation of program structures.
Difference in interpreted and compiled languages.
IB107 Computability and Complexity
zk 2/1 3 kr., podzim
 doc. RNDr. Jan Strejček, Ph.D.
 Prerequisities:
IB005  IB102
 Goals:
The course introduces basic approaches and methods for classification of problems with respect to their algorithmic solvability. It explores theoretical and practical limits of computers usage and consequences these limitations have for advancing information technologies.
At the end of the course the students will be able: to understand basic notions of computability and complexity; to understand the main techniques used to classify problems (reductions, diagonalisation, closure properties), and to apply them in some simple cases.  Learning outcomes:
After enrolling the course student will be able to:
 use asymptotic notation, both actively and passively;
 explain difference between complexity of an algorithm and of a problem;
 independently decide to which complexity class a given problem belongs;
 do practical decisions based on a complexity classification of a particular problem;
 explain that some problems are not computable, give examples of such problems;
 explain the difference between various classes of notcomputable problems;  Syllabus:
Algorithms and models of computation.
Church thesis.
Classification of problems. Decidable, undecidable, and partially decidable problems. Computable functions.
Closure properties. Rice theorems.
Computational complexity. Feasible and unfeasible problems.
Reduction and completeness in problem classes. Manyone reduction and polynomial reduction. Complete problems with respect to decidability, NPcomplete problems. Applications.
IB109 Design and Implementation of Parallel Systems
zk 2/0 2 kr., jaro
 prof. RNDr. Jiří Barnat, Ph.D.
 Prerequisities: The knowledge of lowlevel programming in C is expected at the level PB071.
 Goals: The goal of this course is to introduce to students the principles of design and implementation of parallel systems and get them acquainted with the programmer's means for their development.
 Learning outcomes: On successful completion, students should understand the principles of design and implementation of parallel algorithms and should have limited experience with programmer's means for their development. In particular, students should be able to design and implement their own parallel applications, they should know how to use selected libraries supporting the development of parallel applications, and should be able to explain what is behind the API calls to such libraries.
 Syllabus: Motivation for parallel computing. Parallel algorithm design  decomposition and communication. Analyzes of parallel algorithms. Parallel algorithms in sharedmemory.OpenMP, Intel TBB. POSIX Threads. Lockfree algorithmics. Parallel algorithms in distributedmemory. Message Passing Interface (MPI). Examples of parallel graph algorithms. Parallel algorithms for manycore platforms.
IB110 Introduction to Informatics
zk 2/2 3 kr., jaro
 RNDr. Petr Novotný, Ph.D.
 Prerequisities:
! IB005  ! IB107
none  Goals: The main objective of the course is to acquaint the students with basic abstract computational models and their use in analysis of algorithms and computational problems. At the end of the course, the students will understand fundamental concepts in the theory of finite automata, computability and complexity theory. They will be able to leverage the knowledge of these concepts for deeper understanding of concepts appearing in a practice of programming.
 Learning outcomes:
Successful course graduates will be able to:
 explain the notion of a finite automaton and construct finite automata for simple regular languages
 explain the notion of a regular expression and construct REs for simple regular languages
 explain the concept of nondeterminism and use nondeterminism to simplify the construction of finite automata
 use the basic algorithms for handling of finite automata (determinisation etc.)
 understand the notion of decidability and explain the existence of undecidable problems
 explain the concept of a Turing machine and construct TMs for simple problems
 understand the concept of reduction between computational problems
 understand the concept of computational complexity, the basic complexity classes and relationships between them  Syllabus:
Finite automata and regular languages. Construction of finite automata.
Nondeterministic automata, the use of nondeterminism, determinisation, minimalisation.
Regular expressions and regular grammars. Examples of nonregular languages.
Computational problems and algorithms. Turing machines. Decidable and undecidable problems, diagonalisation.
Reductions between computational problems.
Time and space complexity of algorithms and problems. Classes P and NP. NPcomplete problems. Examples of complexity classes and relationships between them.
IB111 Foundations of Programming
zk 2/2 4 kr., podzim
 RNDr. Nikola Beneš, Ph.D.
 Prerequisities:
! IB113 && ! NOW ( IB113 )
 Goals: The course is an introduction to programming and algorithmic style of thinking.
 Learning outcomes: At the end of the course students should be able to: understand and apply basic constructs of programming languages (e.g., conditions, loops, functions, basic data types); write and debug a program in Python; use basic data types and structures (strings, lists, dictionaries); describe several basic algorithms; describe main conventions and recommended programming style.
 Syllabus:
The course shows the basic elements of imperative programming and algorithmic thinking using the highlevel programming language Python as an example.
Basic notions of imperative programming languages: variables and their semantics, expressions and statements, branching, cycles; subroutines (functions), passing parameters (calling functions), pure functions, predicates.
Numerical computation, basic data types, using the random generator.
Data structures, ADT, lists, strings, multidimensional arrays, sets, dictionaries, the basic of using objects to create userdefined data structures.
The basics of testing and debugging, preconditions and postconditions, type annotation.
Examples of basic algorithms: greatest common divisor, prime numbers, sorting algorithms, searching.
The efficiency of algorithms, the basics of complexity, the complexity of basic data structures operations.
Recursion and its specifics in the imperative paradigm, tail recursion; using recursion to work with tree data structures and to solve constraint satisfaction problems (the basics of the backtracking technique).
Interaction with the environment (I/O), turtle graphics, bitmap graphics, text processing.
Program design, programming styles and conventions, readability and maintainability of code, documentation and comments.
IB113 Introduction to Programming and Algorithms
zk 2/2 4 kr., podzim
 doc. Mgr. Radek Pelánek, Ph.D.
 Prerequisities:
! NOW ( IB111 ) && ! IB111 && ! PB162 && ! PB161 && ! PB071 && ! IB001
 Goals: The course is an introduction to programming and algorithmic style of thinking. At the end of the course students should be able to: understand and apply basic constructs of programming languages (e.g., conditions, loops, functions, basic data types) and know several basic algorithms.
 Learning outcomes:
After finishing this course, a student should be able to:
 use basic tools of structured imperative programming languages (variables, conditions, loops, functions, record data types);
 write and debug a simple Python program and adhere to recommended principles of programming style;
 use basic data types and structures (strings, lists, dictionaries);
 explain several classical algorithms.  Syllabus:
Basic constructions of imperative programming languages: conditions, loops, data types, functions, input, output.
Number types, randomness, algorithms with numbers.
Data types, lists, dictionaries, objects.
Basic algorithms: prime numbers, sorting, searching. Complexity of algorithms (basics).
Turtle graphics, bitmap graphics, regular expressions, text processing.
IB114 Introduction to Programming and Algorithms II
zk 2/1 3 kr., jaro
 prof. RNDr. Ivana Černá, CSc.
 Prerequisities:
( IB111  IB113 ) && ! IB002 && ! NOW ( IB002 )
This course is intended for the study program Cybersecurity. Students enrolled in programs Informatics and Programming and Application Development are recommended IB002 instead.  Goals: The course presents basic data structures and algorithms. Students should correctly apply the basic data structures and algorithms as well as apply the algorithm design and analysis techniques when designing new algorithms. Students implement their algorithms in programming language Python.
 Learning outcomes:
After enrolling the course students are able to:
 actively use basic sorting algorithms and graph algorithms,
 actively design simple algorithms,
 actively used basic static and dynamic data structures,
 employ time complexity and correctness of algorithms,
 implement simple algorithms in the selected programming language (Python).  Syllabus:
Basic analysis of algorithms.
The correctness of algorithms, input and output conditions, partial correctness, convergence, verification.
Length of computation, algorithm complexity, problem complexity. Asymptotical analysis of time and space complexity, growth of functions.
Fundamental data structures. Lists, queues. Representation of sets, hash tables. Binary heaps. Binary search trees.
Sorting algorithms. Quicksort, Mergesort, Heapsort.
Graphs and their representation. Graph search. Depthfirst traversal and Breadthfirst traversal, applications.
IA006 Selected topics on automata theory
zk 2/1 3 kr., podzim
 prof. RNDr. Mojmír Křetínský, CSc.
 Prerequisities: Knowlegde corresponding to the courses IB005  Formal languages and automata and IB107  Computability and complexity
 Goals: The main aim is to understand and explain selected advanced parts of automata theory, including parsing techniques for deterministic contexfree languages, relationship between finitestate automata and MSO logic, automata on infinite words, and process specifications. Further, students should be able to make reasoned decisions about computational models appropriate for the respective areas and to understand methods and techniques of their applications.
 Learning outcomes: At the end of the course students should be able to understand and explain selected advanced parts of automata theory, and to make reasoned decisions about computational models appropriate for the respective area and to understand methods and techniques of their applications.
 Syllabus:
Methods of syntactic analyses of detCFLs.
LL(k) grammars and languages, properties and analyzers.
LR(k) grammars and languages, properties and analyzers.
Relationships between LL, LR and detCFL.
Infinite=state transition systems and nondeterminism  bisimulation. Selected decidable problems related to process verification.
Finitestate automata and monadic secondorder logic
Automata and infinite words: infinite words, regular (rational) sets of infinite words.
Automata: deterministic and nondeterministic Buchi automata, Muller, Rabin, and Street automata. McNaughton theorem. Relationships.
IA008 Computational Logic
zk 2/2 3 kr., jaro
 Dr. rer. nat. Achim Blumensath
 Goals: At the end of the course students should be familiar with main research and applications in computational logic; They will be able to use automatic provers for propositional and predicate logic and also for its extensions; They will be familiar with, and able to use, methods for inductive inference in those logics;
 Learning outcomes: After successfully completing this course students should be familiar with several logics, including propositional logic, firstorder logic, and modal logic. They should be familiar with various proof calculi for these logics and be able to use such calculi to test formulae for satisfiability and or validity. In addition, they should have basic knowledge about automatic theorem provers and they way these work.
 Syllabus:
Resolution for propositional logic.
Resolution for firstorder logic.
Prolog.
Fundamentals of database theory.
Tableaux proofs for firstoder logic.
Natural deduction.
Induction.
Modal logic.
Manyvalued logics.
IA010 Principles of Programming Languages
zk 2/0 2 kr., podzim
 Dr. rer. nat. Achim Blumensath
 Prerequisities: Knowledge of at least one imperative (e.g. C/C++/Java) and one functional language (e.g. Haskell). Knowledge of additional programming languages is an advantage.
 Goals:
By the end of the course, the student will be able:
to understand the various features of a given programming language , including their advantages and disadvantages;
to choose a programming language and programming paradigm suitable for a given problem domain;
to analyse both strong and weak aspects of a given programming language;
to quickly obtain an indepth understanding a of new programming language;  Learning outcomes: After successfully completing this course students will be familiar with the most common features of programming languages. They will know how these features can be used. They will be able to discuss which features can be used to solve a given programming problem and the advantages and disadvantages of the various options.
 Syllabus:
Brief history of programming languages.
Expressions and functions. Scoping. Functional programming.
Types and type checking. Polymorphism. Type inference.
State and side effects. Imperative Programming.
Modules. Abstract data types.
Control flow. Continuations. Generators. Exceptions. Algebraic effects.
Declarative Programming. Single assignment variables. Unification. Backtracking.
Object oriented programming. Dynamic Dispatch. Subtyping. Encapsulated state. Inheritance.
Concurrency. Fibres. Message passing. Shared memory.
IA011 Programming Language Semantics
zk 2/1 3 kr., jaro
 prof. RNDr. Antonín Kučera, Ph.D.
 Prerequisities: Students should be familiar with basic notions of set theory and formal logic (validity and provability, correctness and completeness of deductive systems, etc.)
 Goals: An introduction to the theory of formal semantics of programming languages (operational, denotational, and axiomatic semantics).
 Learning outcomes:
After graduation, student will:
understand basic types of formal semantics of programming languages;
be able to reason about properties of programs using formal semantics;
understand basic notions of temporal logics.  Syllabus:
Formal semantics of programming languages, basic paradigms
(operational, denotational, and axiomatic approach).
Structural operational semantics and its variants (smallstep and bigstep semantics).
Denotational semantics. Complete partial orders, continuous functions. The fixedpoint theorem and its applications, semantics of recursion. Equivalence of operational and denotational semantics.
Axiomatic semantics. Hoare's deductive system, its correctness and completeness.
Temporal logics; the semantics of nonterminating and parallel programs.
IA012 Complexity
zk 2/0 3 kr., podzim
 prof. RNDr. Ivana Černá, CSc.
 Prerequisities: The course expands on course IB107 Computability and Complexity.
 Goals: Theory of computational complexity is about quantitative laws and limitations that govern computing. The course explores the structure of the space of of computable problems and develops techniques to reduce the search for efficient methods for the whole class of algorithmic problems to the search for efficient methods for a few key algorithmic problems. The theory classifies problems according to their computational complexity into feasible and unfeasible problems. Finally, the course tries to understand unfeasability can be coped with the help of techniques like randomization, approximation and parallelization. The main goal of the course is to provide a comfortable introduction to moder complexity theory. While choosing the relevant topics, it places premium on choosing topics that have a concrete relationship to algorithmic problems. Students should understand and analyze complexity issues of basic algorithmic problems and compare different computing approaches.
 Learning outcomes:
After enrolling the course students are able to:
 actively work with computational complexity of problems and algorithms,
 analyse upper and lower bounds of computational complexity,
 differentiate between tractable and untractable problems,
 define basic complexity classes and analyze their relationships,
 explain (NP) hardness and prove hardness of computational problems,
 describe limits of determicnistic, nondeterministic, alternating, randomized, and parallel computing paragigms.  Syllabus:
The structure and properties of time complexity classes. Relation
between determinism and nondeterminism.
The structure and properties of space complexity classes. Relation between determinism and nondeterminism. closure properties of space complexity classes.
Unfeasible problems. Hierarchy of complexity classes. Polynomial hierarchy. Relativization. Nonuniform computational complexity.
Randomized complexity classes and their structure. Approximative complexity classes and nonapproximability.
Alternation and games. Interactive protocols and interactive proof systems.
Lower bounds techniques. Kolmogorov complexity.
Descriptive complexity.
IA014 Advanced Functional Programming
zk 2/0 2 kr., jaro
 doc. Mgr. Jan Obdržálek, PhD.
 Prerequisities: Previous experience with functional programming, at least to the extent covered by the course IB015  Nonimperative programming.
 Goals: Introduce the theoretical concepts behind the functional programming paradigm, i.e. lambdacalculus and various type systems. Present some of the modern advanced functional programming concepts (typeclasses, monads, monad transformers, GADTs, dependent types...).
 Learning outcomes:
By the end of the course, students will:
understand the theoretical foundations of functional programming, e, g, lambda calculi and type theory;
understand and be able to efficiently use modern/advanced concepts of functional programming languages (e.g. typeclasses, monads, monad transformers...);
know the limits of the functional programming paradigm;
be able to evaluate and use FPbased concepts in modern mainstream (nonFP) languages.  Syllabus:
History of functional programming languages.
Untyped lambda calculus.
Simply typed lambda calculus.
Polymorphism add type inference (HindleyMilner, System F)
Type classes.
Functors, Applicatives.
Monads.
Monad tranformers.
GADTs  Generalized Algebraic Data Types
Dependent types.
IA023 Petri Nets
zk 2/0 2 kr., jaro
 prof. RNDr. Antonín Kučera, Ph.D.
 Prerequisities: Students should be familiar with basic notions of computability, complexity, and automata theory.
 Goals: An introduction to Petri nets; the course covers both "classical" results (about boundedness, liveness, reachability, coverability, etc.) and "modern" results (the (un)decidability of equivalencechecking and modelchecking, etc.)
 Learning outcomes: At the end of the course, students should be able to: understand the language of Petri nets; model various classes of systems using Petri nets; apply specific analytical techniques developed for Petri nets; prove properties of discrete systems using Petri nets and appropriate specification formalisms.
 Syllabus:
The theory of Petri nets provides a formal basis for modelling,
design, simulation and analysis of complex distributed
(concurrent, parallel) systems, which found its way to
many applications in the area of computer software, communication protocols, flexible manufacturing systems, software engineering, etc.
Principles of modelling with Petri nets.
Classical results for place/transition nets. Boundedness, coverability, KarpMiler tree, weak Petri computer; reachability and liveness.
(Un)decidability of equivalencechecking and modelchecking with place/transition nets.
Ssystems, Tsystems. Reachability, liveness, Sinvariants, Tinvariants.
Freechoice Petri nets. Liveness, Commoner's theorem.
IA038 Types and Proofs
zk 2/0 2 kr., jaro
 prof. RNDr. Jiří Zlatuška, CSc.
 Goals: This course delivers focuses on the correspondence between proof theory and typed lambdacalculus and its generalization to the correspondence between computations as proof simplifications and program specifications as types in various formal settings. The contents of the course is relevant for work in many areas of theoretical computer science.
 Syllabus:
Meaning and denotation in logic, tarski and Heyting.
natural deduction: calculus, rules, computational interpretation.
CurryHoward isomorphism: lambdacalculus, operational and denotational interpretation, conversion, isomorphism.
Normalization theorem: ChurchRosser property, weak normalization, strong normalization.
Sequent calculus: structural rules, intuitionistic version, identities, logical rules, properties of the cutfree system, translation between sequent calculus and natural deduction.
Strong normalization theorem: reducibility and its properties.
Gödels system T, calculus, normalization, expressive power.
Coherent spaces, stabil functions, paralel disjunction, product and function spaces, denotational semantics of System T.
Sums in natural deduction: problems, standard conversion, commuting conversions, functional calculus.
System F: calculus, simple types, free structures, inductive types, CurryHoward isomorphism, strong normalization.
Coherent semantics of the sum; cutelimination theorem; representation.
IA040 Modal and Temporal Logics for Processes
zk 2/0 2 kr., podzim
 prof. RNDr. Luboš Brim, CSc.
 Prerequisities: Recommended: IV010 Communication and Parallelism
 Goals:
The aim is to acquire basic knowledge and develop skills in the use of modal and temporal logics for specification, analysis and verification of computer systems.
By the end of the semester, students should be able:
to understand definitions of the logics and logical systems presented in the course, the ideas of the proofs;
to understand the differences in the logics, and their capabilities and limitations;
to understand the utility, potential and limitations of formal verification, in particular model checking, for lineartime and branchingtime logics.  Learning outcomes: By the end of the semester, students should be able: • to understand and apply definitions of the logics and logical systems presented in the course, the ideas of the proofs; • to understand the differences in the logics, and their capabilities and limitations; • to understand the utility, potential and limitations of formal verification, in particular model checking, for lineartime and branchingtime logics.
 Syllabus:
Modal logics: propositional modal logic, modal mucalculus.
Temporal logics: propositional temporal logic, linear and branching time, temporal operators.
Classification of properties, liveness, safety, local and global properties.
Model checking, applications.
IA041 Concurrency Theory
k 0/2 2 kr., jaro
 prof. RNDr. Mojmír Křetínský, CSc.
 Prerequisities:
IA006
Knowlegde corresponding to the courses IA006  Automata IB107  Computability and complexity  Goals: Students should study, understand, present and to work with the basic concepts and techniques used for modelling, analysis and verification of concurrent processes.
 Learning outcomes:
At the end of the course students should be able:
to understand and to work with the basic techniques used for modelling, analysis and verification of concurrent processes;
to make deductions based on acquired knowledge on actual topics and results of concurrent processes and their formal verification.  Syllabus:
Processes, labelled transition systems and their (finite) specifications.
Operational semantics. Caucal a Mayr hierarchies.
Selected sematic equivalencies (and preorders) for processes and their relationships (linear time  branching time spectrum).
Boundaries of algorithmic verification (equivalence checking)  undecidability, decidability and complexity of some semantic equivalencies on selected classes of infinite state processes.
IA046 Computability
zk 2/0 2 kr., jaro
 prof. RNDr. Luboš Brim, CSc.
 Prerequisities: Prerequisities: IB107 Computability and Complexity
 Goals:
The course is focused on deeper understanding of results in the computability theory with emphasis on methods and techniques used to prove such results.
At the end of the course the students will be able to understand basics of computability over real numbers; will get acquainted with additional results about classification of computational problems, in particular about arithmetical hierarchy and relativised theory of computability.  Syllabus:
Recursion theorem, generalized Rice theorem, Rogers isomorphism theorem.
Application to logic. Arithmetical sets and functions, GoedelRosser incompleteness theorem. Goedel's second incompleteness theorem.
Relativised computability. Programs with oracles.
Kleene hierarchy, Turing reducibility, ttreducibility, arithmetical hierarchy.
Post's problem.
Analytical hierarchy.
Computability on real numbers, complete partial orders, domains.
IA062 Randomized Algorithms and Computations
zk 2/2 3 kr., jaro
 prof. RNDr. Jozef Gruska, DrSc.
 Prerequisities: No special requirements are needed.
 Goals: The aim: randomized algorithms and methods are becoming one of the key tools for an effective solution of a variety of problems in informatics and its aplications practically in all theoretical and aplication areas.
 Learning outcomes: After finishing the lecture student will be able: To manage basic techniques to design randomized algorithms; to understand differences concerning power of deterministic and randomized algorithms; to manage basic tools for analysis of randomized algorithms; to work with tail inequalities; to understand power and use of the probabilistic method; to understand power of random walks; to understand power of randomized proofs; to understand basic principles of randomized cryptographic protocols.
 Syllabus:
Randomized algorithms and methods.
Examples of randomized algorithms.
Methods of game theory.
Main types of randomized algorithms.
Randomized complexity classes.
Chernoff's bounds.
Moments and deviations.
Probabilistic methods.
Markov chains and random walks.
Algebraic methods.
Aplications:
Linear programming.
Parallel and distributed algoritms.
Randomization in cryptography.
Randomized methods in theory of numbers.
IA066 Introduction to Quantum Computing
zk 2/0 2 kr., podzim
 prof. RNDr. Jozef Gruska, DrSc.
 Prerequisities:
IB005  IB102
linear algebra, automata and languages, no quantum physics is necessary, algorithm design  Goals: Quantum computing in particular and quantum information processing in general are one of the hotest subjects in science in general and in informatics in particular. The goal of this introductory course is to present basic aims, concepts, methods and result in this fascinating area.
 Learning outcomes: After completing the course student will be able: to understand principles of the design of quantum algorithms; to understand basic ideas of Shor's and Grover's algorithms; to design simple quantum circuits; to understand recognition power of several quantum automata; to understand basic principles of quantum cryptography  theory, experiment and practical systems; to design quantum errorcorrecting codes.
 Syllabus:
Motivácie, historia, základné kvantové experimenty,
ohraničenia a paradoxy kvantového spracovania informácie
Hilbertové priestory, kvantové bity, registre, hradla a obvody
kvantové výpočtové primitíva
kvantové entanglovanie a nelokálnost
jednoduché kvantové algoritmy, Shorove kvantové algoritmy, algoritmus Grovera a jeho aplikácie
kvantové konečné automaty
kvantové samoopravujúce kody a kvantové faulttolerantné hradla.
kvantová krzptografia
vesmír ako kvantový systém
IA067 Informatics Colloquium
z 1/0 1 kr., podzim
 prof. RNDr. Daniel Kráľ, Ph.D., DSc.  doc. RNDr. Barbora Kozlíková, Ph.D.
 Goals: The aim of the colloquium is to present new directions, methods and results in informatics, broadly understood. Talks will cover all areas of informatics and related areas and will be given by wellknown specialists, especially outside of Brno and from abroad.
 Learning outcomes: After finishing the course students will have updated information about recent research provided by faculties and also by specialists from other academic instituition, also from abroad. For each presented area student will be able to decide whether its techniques can be used to solve a particular theoretical or application problem.
 Syllabus: The aim of the colloquium is to present new directions, methods and results in informatics, broadly understood. Talks will cover all areas of informatics and related areas and will be given by wellknown specialists, especially outside of Brno and from abroad.
IA067 Informatics Colloquium
z 1/0 1 kr., jaro
 prof. RNDr. Daniel Kráľ, Ph.D., DSc.  doc. RNDr. Barbora Kozlíková, Ph.D.
 Goals: The aim of the colloquium is to present new directions, methods and results in informatics, broadly understood. Talks will cover all areas of informatics and related areas and will be given by wellknown specialists, especially outside of Brno and from abroad.
 Learning outcomes: After finishing the course students will have updated information about recent research provided by faculties and also by specialists from other academic instituition, also from abroad. For each presented area student will be able to decide whether its techniques can be used to solve a particular theoretical or application problem.
 Syllabus: The aim of the colloquium is to present new directions, methods and results in informatics, broadly understood. Talks will cover all areas of informatics and related areas and will be given by wellknown specialists, especially outside of Brno and from abroad.
IA072 Seminar on Verification
z 0/2 2 kr., podzim
 doc. RNDr. Jan Strejček, Ph.D.
 Prerequisities:
souhlas
for postgraduate students; undergraduate students interested in formal methods may ask for an exception, especially if they are interested in program analysis or automata theory.  Goals:
The aim of the course is to
introduce students to selected research areas;
check their ability to understand a scientific paper;
check and improve their skill of presenting a scientific paper;  Learning outcomes:
At the end of the course students should be able to:
understand a theoretical scientific text;
make a presentation that explains main ideas of such a text;
potentially apply gathered knowledge in an original research;  Syllabus:
Presentations of results from the following areas:
Analysis and verification of software.
Automata and logics over infinite words.
Satisfiability and theorem proving.
IA072 Seminar on Verification
z 0/2 2 kr., jaro
 doc. RNDr. Jan Strejček, Ph.D.
 Prerequisities:
souhlas
for postgraduate students; undergraduate students interested in formal methods may ask for an exception, especially if they are interested in program analysis or automata theory.  Goals:
The aim of the course is to
introduce students to selected research areas;
check their ability to understand a scientific paper;
check and improve their skill of presenting a scientific paper;  Learning outcomes:
At the end of the course students should be able to:
understand a theoretical scientific text;
make a presentation that explains main ideas of such a text;
potentially apply gathered knowledge in an original research;  Syllabus:
Presentations of results from the following areas:
Analysis and verification of software.
Automata and logics over infinite words.
Satisfiability and theorem proving.
IA077 Advanced Quantum Information Processing
zk 2/0 2 kr., jaro
 prof. RNDr. Jozef Gruska, DrSc.
 Prerequisities:
IA066
Prerequisities: course IA066 Introduction to quantum computing or agreement with the lecturer  Goals: To present advanced basic concepts of quantum information processing to such an extend that participants could get involved in research in this area. Lecture should be of interest and importance especially to those with a deeper interest in quantum information processing: projects, diploma thesis, doctarate study.
 Learning outcomes: Understanding and ability to use basic terms such as entropy, quantum canals. Understanding and ability to explain fundaments of quantum entangling.
 Syllabus:
Mixed states and density matrices, quantum operations and
their representations, projective and POVM measurements and their
applications
quantum information theory  entropies, quantum channel capacities
quantum entanglement and multipartite quantum entanglement (structure, measures, sharing laws and limitations), quantum error correction and faulttolerant systems
quantum nonlocality
new paradigms in quantum information processing.
IA080 Seminar on Knowledge Discovery
k 0/2 2 kr., podzim
 doc. RNDr. Lubomír Popelínský, Ph.D.
 Goals: At the end of the course students should be able to understand scientific works in the area of machine learning and knowledge discovery in data and use it in their work. They will be able to evaluate contributions of such research studies.
 Learning outcomes:
A student will be able
 to understand research papers from machine learning and data mining;
 of critical reading of such papers;
 to prepare and present a lecture on advanced methods of data science.  Syllabus: The seminar is focused on machine learning and theory and practice of knowledge discovery in various data sources. Program of the seminar contains also contributions of teachers and PhD. students of the Knowldge Discovery Laboratory, as well as other laboratories, on advanced topics of knowledge discovery.
IA080 Seminar on Knowledge Discovery
k 0/2 2 kr., jaro
 doc. RNDr. Lubomír Popelínský, Ph.D.
 Prerequisities: Prerequisite for enrollment in the subject is 1) being familiar with advanced machine learning 2) approval of the application by the teacher
 Goals: At the end of the course students should be able to build and evaluate advanced machine learning systems and to understand scientific works in the area of machine learning and data science and use it in their work. They will be able to evaluate contributions of such research studies.
 Learning outcomes:
A student will be able
 to understand research papers from machine learning and data mining;
 of critical reading of such papers;
 to prepare and present a lecture on advanced methods of data science.  Syllabus: The seminar is focused on machine learning and theory and practice of knowledge discovery in various data sources. Program of the seminar contains also contributions of teachers and PhD. students of the Knowldge Discovery Laboratory, as well as other laboratories, on advanced topics of knowledge discovery.
IA082 Physical concepts of quantum information processing
zk 2/0 2 kr., jaro
 RNDr. Daniel Reitzner, PhD.  doc. Mgr. Mário Ziman, Ph.D.
 Prerequisities:
PV275  SOUHLAS
 Goals: Introduction to quantum physics and quantum information theory.
 Learning outcomes:
After this course students should:
understand basic principles of quantum physics;
apply the learned concepts in the subsequent study of quantum information theory;
selfstudy quantum theory books.  Syllabus:
1. Security and computation with photons
 photon's polarization and polarizers, Vernam cipher, quantum key "distribution" protocol B92, polarizing beamsplitter, √NOT logic gate,
2. Quantum interference and superposition  MachZender interferometer, concept of quantum state, quantum probabilities and amplitudes, Hilbert space and operators,
3. Measuring quantum properties  description of quantum measurement devices (POVM), tomography of polarization, uncertainty relations, no information without disturbance
4. Hydrogen atom  emission spectrum, Bohr's model, position and momentum, quantum solution, Zeeman effects, spin of electron,
5. Schrodinger equation  time and evolution, unitary operators, energy conservation and system's Hamiltonian,
6. Quantum bit  twolevel quantum system (polarization and spin1/2), SternGerlach experiments, Bloch sphere, orthogonality and information, nocloning theorem, quantum NOT gate, qubit implementations
7. Quantum sources and randomness  mixed states, quantum commpression, von Neumann entropy, capacity of noiseless quantum channel, randomness sources, minentropy
8. EinsteinPodolskiRosen paradox  composite quantum systems, tensor product, quantum steering, EPR paradox, local hidden variable model, CHSH inequalities, experiments and loopholes
9. Quantum onetime pad protocols  onetime pad, superdense coding and teleportation
10. Quantum entanglement  correlated and separable states, definition of entanglement, entanglement distilation,
11. Quantum cryptography  QKD protocols BB84, E91, noquantum bit commitment theorem, quantum secret sharing protocols,
12. Elementary particles  fermions and bosons and tensor products, standard model, Higg's boson
IA101 Algorithmics for Hard Problems
zk 2/0 2 kr., podzim
 prof. RNDr. Ivana Černá, CSc.
 Prerequisities: Experience with basic techniques for design and analysis of algorithms (recursion, dynamic programming, greedy approach) as well as with basic data structures and algorithms are required.
 Goals: The course expands on courses IB002 Algorithms and Data Structures I and IV003 Algorithms and Data Structures II. It focuses on design of algorithms for hard computing tasks. The course systematically explains, combines, and compares the main possibilities for attacking hard algorithmic problems like randomization, heuristics, approximation and local search.
 Learning outcomes:
After enrolling the course students are able to :
 identify algorithmically hard problems,
 identify applications where pseudopolynomial, approximative, randomized, and heuristic algorithms can be succesfully used,
 actively used published pseudopolynomial, approximative, and randomized algorithms and correctly interpret their outcomes,
 design simple pseudopolynomial, approximative, and randomized, algorithms,
 experimentally evaluate heuristic algorithms.  Syllabus:
Deterministic approaches: pseudopolynomialtime algorithms,
parametrized complexity, branchandbound, lowering worst case
complexity of exponential algorithms.
Approximation approaches: concept of approximation algorithms, classification of optimization problems, stability of approximation, inapproximability, algorithms design. Linear programming as a method for construction of approximative algorithms.
Randomized approaches: classification of randomized algorithms and design paradigms, design of randomized algorithms, derandomization, randomization and approximation.
Heuristics: local search, simulated annealing, genetic algorithms.
IA158 Real Time Systems
zk 2/0 3 kr., jaro
 doc. RNDr. Tomáš Brázdil, Ph.D.
 Prerequisities: Basic programming skill in C is expected.
 Goals: At the end of the course students should: know specific aspects of realtime systems; understand main problems of the design of realtime systems and know some solutions; be able to use formal reasoning about realtime systems.
 Learning outcomes: At the end of the course student will have a comprehensive knowledge of real time systems and related areas. Will be able to distinguish basic types of realtime systems. Will be aware of typical design errors in realtime and embedded systems and their standard solutions. Will understand fundamental realtime scheduling and resource management algorithms. Will have a basic knowledge of implementation details of these algorithms in standard programming environments.
 Syllabus:
Realtime aspects of embedded systems; examples of realtime systems. Soft and hard realtime systems.
Realtime scheduling: periodic and aperiodic tasks, prioritydriven scheduling, resource access control.
Basic information about realtime operating systems and programming.
IA159 Formal Verification Methods
zk 2/0 2 kr., jaro
 doc. RNDr. Jan Strejček, Ph.D.
 Prerequisities:
IA169
 Goals:
At the end of this course, students should understand and be able to explain principles, advantages, and disadvantages of selected methods from the area of formal verification, namely model checking methods, abstraction, static analysis via abstract interpretation, and shape analysis;
make reasoned decisions about suitability of various methods for verification of specific systems;  Learning outcomes:
At the end of this course, students should understand and be able to explain principles, advantages, and disadvantages of selected methods from the area of formal verification, namely model checking methods, abstraction, static analysis via abstract interpretation, and shape analysis;
make reasoned decisions about suitability of various methods for verification of specific systems;  Syllabus:
Overview of formal verification methods.
LTL model checking of finite and infinitestate systems including partial order reduction.
Abstraction.
Counterexampleguided abstraction refinement (CEGAR).
Static analysis, abstract interpretation.
Shape analysis.
Software verification via automata, symbolic execution, and interpolation.
PropertyDirected Reachability (PDR/IC3).
IA161 Natural Language Processing in Practice
k 1/1 2 kr., podzim
 doc. RNDr. Aleš Horák, Ph.D.  Bc. Adam Hájek  RNDr. Miloš Jakubíček, Ph.D.  RNDr. Marek Medveď  RNDr. Zuzana Nevěřilová, Ph.D.  RNDr. Adam Rambousek, Ph.D.  doc. Mgr. Pavel Rychlý, Ph.D.  RNDr. Vít Suchomel, Ph.D.
 Prerequisities: All students should have basic practical knowledge of programming in Python. Overview knowledge of the natural language processing field at the level of introductory courses such as IB030 Introduction to Computational Linguistics or PA153 Natural Language Processing is expected. The seminar is given in English. Task solutions can be in English, Czech or Slovak.
 Goals: The course participants will have the opportunity to learn about, test and experiment with advanced techniques of natural language processing (NLP) and to develop an understanding of the limits of those techniques. The course aims to introduce current research issues, and to meet in practice with particular programming techniques used in language technology applications.
 Learning outcomes:
After studying the course, the students will be able to:
 explain a selected NLP problem and list its main aspects;
 implement a basic or intermediate application for complex tasks in language processing, typically for Czech, Slovak, or English;
 create data resources (models, test sets) for a selected NLP problem and evaluate their assets;
 compare selected available tools for complex NLP tasks and apply them to chosen data resources with possible adaptations to particular purposes.  Syllabus:
The presented NLP problems will concentrate on practical problems connected with processing humanproduced textual data. Particular topics include:
 Opinion mining, sentiment analysis
 Machine translation
 Parsing of Czech: Between Rules and Statistics
 Named Entity Recognition
 Building Language Resources from the Web (effective crawling, boilerplate removal, tokenisation, near duplicates identification)
 Language modelling
 Topic identification, topic modelling
 Extracting structured information from text
 Automatic relation extraction (hypernyms, synonyms, ...)
 Adaptive electronic dictionaries
 Terminology identification (keywords, key phrases)
 Anaphora resolution
 Stylometry
 Automatic language corrections
IA168 Algorithmic game theory
zk 2/0 3 kr., podzim
 doc. RNDr. Tomáš Brázdil, Ph.D.
 Prerequisities: basic linear algebra, basic probability theory (mostly discrete probability), elementary complexity theory, some calculus
 Goals: In recent years, huge amount of research has been done at the borderline between game theory and computer science, largely motivated by the emergence of the Internet. The aim of the course is to provide students with basic knowledge of fundamental game theoretic notions and results relevant to applications in computer science. The course will cover classical topics, such as general equilibrium theory and mechanism design, together with modern applications to network routing, scheduling, online auctions etc. We will mostly concentrate on computational aspects of game theory such as complexity of computing equilibria and connections with machine learning.
 Learning outcomes: Student knows the basics types of models of games and algorithms for searching winning strategies.
 Syllabus:
Basic definitions: Games in normal form, dominant strategies, Nash
equilibria in pure and mixed strategies, existence of Nash equilibria, basic examples
Computing Nash equilibria: LemkeHowson algorithm, support enumeration, sampling methods, PPADcompleteness of Nash equilibria,
Quantifying the inefficiency of equilibria and related games: Congestion and potential games, price of anarchy and price of stability, routing games, network formation games, load balancing games
Learning in games: Regret minimization algorithms, correlated equilibria and connection to learning in games, regret minimization in routing games
Auctions and mechanism design: First price auctions, Vickrey auctions, truthfulness, VickreyClarkGroves mechanism, Bayesian games, Bayesian Nash equilibria, formal framework for mechanism design, revelation principle, auctions on Google
Games with multiple moves: Games in extensive form, games on graphs, Markov decision processes, stochastic games
IA169 System Verification and Assurance
zk 2/0 4 kr., podzim
 prof. RNDr. Jiří Barnat, Ph.D.
 Prerequisities:
(! IV113 ) && (! NOW ( IV113 ))
Userlevel familiarity with Unix/Linux operating system. Basic programming skills (Python). Some degree of abstract math reasoning.  Goals: The student will understand the necessary theoretic background as well as acquire handson experience with relevant tools for bug finding and formal verification techniques. Students will get acquainted with a number of concrete software verification tools for analysis of concurrent systems, realtime systems, hybrid systems, cryptographic systems, and systems with probabilities.
 Learning outcomes:
Students will:
be aware of fundaments of blackbox testing;
understand priciples of deductive verification;
understand the theory and application of model checking;
have handon experince with a couple of verification tools.  Syllabus: This course will provide the necessary theoretic background as well as handson experience with relevant tools for bug finding and formal verification techniques. The core topics of this course will include testing, symbolic execution, abstract interpretation, static analysis, theorem proving, automated formal verification as well as an introduction to modelbased verification. Students will get acquainted with a number of concrete software verification tools for analysis of concurrent systems, realtime systems, hybrid systems, cryptographic systems, and systems with probabilities. An introductory insight into security standards like Common Criteria for Information Technology Security Evaluation and FIPS 140 shall be provided as well.
IA174 Fundaments of Cryptography
zk 2/0 3 kr., podzim
 RNDr. Petr Novotný, Ph.D.
 Prerequisities: Grasp of basic concepts from discrete mathematics (e.g. groups, see the MB154 and MV008 courses). Awareness of basic aims and building blocks of cryptography, corresponding to the respective parts of the PV080 course.
 Goals: The course covers theoretical foundations of cryptography, ranging from encryption and hashing primitives to more modern topics such as blockchain technologies. We will learn why are the stateoftheart cryptographic algorithms constructed in the way they are, and how to reason about their mechanics and security guarantees via the language of mathematics.
 Learning outcomes:
Upon a successful completion of the course, the student will be able to:
*Explain and understand the mechanics of basic primitives of both symmetric and asymmetric cryptography, including the underlying mathematics.
*Explain and understand the function, construction, and the use of cryptographic hash functions.
*Explain and understand cryptographic techniques for ensuring data authenticity and integrity, including digital signature schemes.
*Understand, at an abstract level, the purpose and foundations of postquantum cryptography, secret sharing, homomorphic encryption, and zeroknowledge proofs, so as to be able to learn further details of these topics on her/his own.
*Understand possible weaknesses of cryptosystems and various tradeoffs in their design.
*Analyse weaknesses of simple cryptosystems.
*Explain and understand the concept of block chain and smart contracts, and be aware of the design principles behind relevant blockchain protocols.  Syllabus:
FUNDAMENTALS
Symmetric cryptography:
*Symmetric block ciphers: design principles and basic notions (boolean functions, random permutations, confusion, diffusion, nonlinearity); design of iterated block ciphers, rounds, key schedules; AES; modes of operations of block ciphers.
*Symmetric stream ciphers: General principles, ChaCha cipher, relation to pseudorandom number generators.
Asymmetric cryptography:
*General principles and design elements, "reductions" to hard problems.
*RSA algorithm: math foundations (modular arithmetic, multiplicative Z_n^x groups, Euler's theorem, Chinese remainder theorem, extended Euclidean algorithm); RSA encryption, possible attacks, relationship to integer factorization.
*Cryptography based on discrete logarithm (DL): refresher of basic group theory; DL in (Z_n )^x groups, DiffieHellman key exchange, DSA; discrete logarithm on elliptic curve groups, elliptic curve cryptography, ECDSA.
Cryptographic hash functions: Design principles, Merkle–Damgård construction, sponge construction, collisionresistant CHFs, Keccak CHF, attacks against CHFs.
Data integrity, message authentication, signatures (2 lectures):
*Message authentication codes (MACs): integrity, authenticity, construction from block ciphers, construction from hash functions; authenticated encryption, AEAD.
*Digital signatures: nonrepudiation, signature schemes (RSA, DSA, ElGamal), attacks against dig. signature schemes, blind signatures.
*Integrity of data structures: hash trees, their use in Bitcoin.
ADVANCED TOPICS:
Postquantum cryptography: Quantumcomputer attacks on RSA and discrete logarithm schemes, overview of candidate techniques for postquantum cryptography, standardization of postquantum cryptography.
Zeroknowledge proofs: mathematical foundations, connection to complexity classes, illustration on concrete problems, zeroknowledge proofs in blockchain systems.
Blockchain technology: blockchain as a general concept of a distributed ledger; Bitcoin, its design, proof of work, mining process; Ethereum, smart contracts, proof of stake; economical and social impact of blockchain technologies.
IV003 Algorithms and Data Structures II
zk 2/2 3 kr., jaro
 prof. RNDr. Ivana Černá, CSc.
 Prerequisities:
( IB002  program ( PřF:N  MA )) && ! IB108
The course expands on courses IB002 Algorithms and Data Structures I.  Goals: The course expands on the introductory course Algortihm Design I. It presents algorithmic concepts without their direct connection to any particular programming language. The aim is to introduce students into design and analysis of advanced algorithms. The course presents advanced techniques of algorithm analysis and a wide spectrum of strategies together with algorithms built up on these strategies. Students are introduced into new data structures which are displayed in a row with algorithms based on them.
 Learning outcomes:
After enrolling the course students are able to:
 actively use and modify advanced graph and string algorithms,
 actively used advanced techniques for designing algorithms (dynamic programming, greedy techniques) for designing algorithms, expain their specific properties and limits,
 actively used and modify advanced dynamic data structures and use them for designing effective algorithsm,
 analyze time complexity and prove correctness of algorithms.  Syllabus:
Advanced design and analysis techniques: dynamic programming, greedy strategies,backtracking. Amortized analysis.
Advanced data structures: binomial and Fibonacci heaps, data structures for disjoint sets.
Graph algorithms: SingleSource Shortest Paths (The BellmanFord algorithm). AllPairs Shortest Paths (Shortest paths and matrix multiplication, The FloydWarshall algorithm, Johnson's algorithm for sparse graphs). Maximum Flow (The FordFulkerson method, The PushRelabel method). Maximum bipartite matching.
String matching: the naive stringmatching algorithm, KarpRabin algorithm, string matching with finite automata. The KnuthMorrisPratt algorithm.
IV010 Communication and Parallelism
zk 2/0 2 kr., jaro
 prof. RNDr. Luboš Brim, CSc.
 Goals:
The goal is to acquire basic skills that are used for formal specification and analysis of communicating systems, including the theoretical background.
By the end of the course the students should be able: to develop simple specifications and implementations of communicating systems in CCS, to check formally their equivalence and to understand various kinds of process equivalences and their limitations.  Learning outcomes: By the end of the course the students should be able: to develop simple specifications and implementations of communicating systems in CCS, to check formally their equivalence and to understand various kinds of process equivalences and their limitations.
 Syllabus:
Introduction, overview of models for concurrent systems. Modelling
communication, examples of communicating systems.
Language of CCS: synchronization, actions and transitions, internal communication, semantics of CCS.
CCS with value passing and its translation into pure CCS.
Equational laws and their applications: classification of combinators, expansion theorem, dynamic and static laws.
Bisimulation and equivalence: Strong bisimulation, weak bisimulation, weak congruence, basic properties, solving equations, other equivalences, finite state processes.
Temporal properties of processes.
IV022 Principles of elegant programming
zk 2/0 2 kr., podzim
 prof. RNDr. Luboš Brim, CSc.
 Goals: Programs are typically constructed from smaller ones, each realizing a particular function. The ability to design small perfect programs seems to be the core skill of every serious programmer. The goal is to get acquainted with methods for designing and verifying small and, at the same time, elegant sequential algorithms. The students acquire basic techniques and principles that can contribute to this goal.
 Learning outcomes: By the end of the semester, students should be able to develop small sequential algorithms and prove their correctness.
 Syllabus:
Garded command language. Skip and abort commands, composition, alternative
command, iterative command.
Verification of programs, proof outlines, verification rules for sequential composition, alternative, and loop commands. Array manipulation.
Constructive verification of programs, basic principles and strategies, developing loops from invariants and bounds, developing invarinats.
Examples of program development. Deriving of efficient algorithms, Searching and sorting.
IV029 Introduction to Transparent Intensional Logic
zk 2/0 2 kr., podzim
 prof. RNDr. Marie Duží, CSc.
 Prerequisities: Foundations of the firstorder predicate logic
 Goals:
Students enrolled in the course will obtain knowledge on a rather new discipline Logical semantics and knowledge representation that belongs to the fundamentals of artificial intelligence.
Adequate analysis of the meaning of natural language expressions consists in discovering algorithmically structured procedure known as TIL construction encoded by the expression. The analysis should be as finegrained as possible so that the inference machine is neither overinferring nor underinferring. At the same time it is necessary to formalize the results of an analysis so that they are computationally tractable.  Learning outcomes: The students will learn to solve relevant problems in such a way that undesirable paradoxes and inconsistencies are avoided. The formalized analysis can be used in knowledgebase systems of artificial intelligence, in automatic translation, in multiagent systems, etc.
 Syllabus:
Deductive reasoning as the subject of logic
Paradoxes stemming from a coarsegrained analysis of premises
FregeChurch semantic schema; denotational vs. procedural semantics
Transparent Intensional Logic; constructions as procedures
Simple theory of types comprising nonprocedural objects; epistemic base; intensions and extensions
Ramified theory of types comprising procedural objects
Extensional, intensional and hyperintensional context
Extensional rules: Leibniz’s law and existential quantification into
The problem of nonexistence and modalities
Ontology as a logic of intensions; conceptual analysis
Logic of attitudes; hyperintensional knowledge representation
Dynamic reasoning and tense logics
Communication of agents in a multiagent system
IV057 Seminar on Information Society
k 0/2 2 kr., podzim
 prof. RNDr. Jiří Zlatuška, CSc.
 Goals: At the end of the course, students should be able to understand better issues related to the information society, gain deeper insight into more specialized topics, and to develop their own ability to interpret technological and social issues related to the information society.
 Learning outcomes: Ability to think about impacts of IT on society.
 Syllabus:
Seminar focuses on some aspects of the Information Society.
A loose companion seminar to IV064 Information Society based on students work
with literature on this topic and talks by the students based
on the literature. Studets are required to work independently with relevant literature.
Information technologies and the society.
Information as a real value.
Economic and social impact.
Civilizational impacts of the Information Society.
Information technologies and democracy.
IV064 Information Society
zk 2/0 2 kr., podzim
 prof. RNDr. Jiří Zlatuška, CSc.
 Prerequisities:
! CORE012 && !( NOW ( CORE012 ))
 Goals: The goal of this course is to introduce the nature of wider impacts of Informatics on the society.
 Learning outcomes: At the end of this course students will be able to understand and explain the nature of wider impacts of Informatics on the society; to use information about events characteristic for the impact of the information revolution; to draw parallels with the industrial revolution; to explain and characterize events and processes associated with the formation of information society; to better comprehend the role of the information and communication technologies in the society not only as technical tools, but also as a phenomenon enabling social processes transformation; to understand newly emerging organizational structures both in business and in egovernment resulting from intensification of the information processing; to understand the nature of innovative processes associated with informatics and to thing through the consequences of differencec from prevailing older paradigms; to grasp idea of the structure of policies assiciated with information society; to present thoughful analyses of nontechnical impacts of widespread availability and use of services based on information processing; to think through and creatively develop designs of new possible applications; to develop motivation for future theoretical or practical work in this area.
 Syllabus:
This course deals with the impact of Information Technologies on society,
with the nature of computer (information) revolution,
and the advent of an information society.
Informatics in historical perspective.
Computer revolution.
Productivity paradox.
The Internet and WWW.
Digital economy.
Network economy and virtual communities.
Organizational and company structure.
Organizational transformation.
Teleceoomunications and information infrastructure.
Legal aspects of an information society.
Ethical problems.
Riskc of computing technology.
Social impacts.
There is a seminar IV057 Seminar on Information Society accompanying this course for students interested in presenting uptodate material based on literature on an information society.
IV074 Laboratory of Parallel and Distributed Systems
z 0/0 2 kr., podzim
 prof. RNDr. Jiří Barnat, Ph.D.  prof. RNDr. Ivana Černá, CSc.
 Prerequisities:
souhlas
Applicants should 1) be able to work independently 2) have interest in longterm projects (several semesters) 3) have working knowledge of English 4) be able to work in a team. The enrollment must be approved by the laboratory head (J. Barnat).  Goals: The goal of this course is to let students participate on research activities.
 Learning outcomes: On successful completion of the course students  will have practical experience with active research  should be able to read and understand scientific papers  should be able to employ gathered information to formulate and prove their own hypotheses within the relevant context.
 Syllabus: Laboratory of Parallel and Distributed Systems (ParaDiSe) is a team project focused on the development of parallel methods and tools for the design and analysis of complex systems. Students meet regularly with senior researchers to discuss research problems related to their research topics.
IV074 Laboratory for Parallel and Distributed Systems
z 0/0 2 kr., jaro
 prof. RNDr. Jiří Barnat, Ph.D.  prof. RNDr. Ivana Černá, CSc.
 Prerequisities:
souhlas
Applicants should 1) be able to work independently 2) have interest in longterm projects (several semesters) 3) have working knowledge of English 4) be able to work in a team. The enrollment must be approved by the laboratory head (J. Barnat).  Goals: The goal of this course is to let students participate on research activities.
 Learning outcomes: On successful completion of the course students  will have practical experience with active research  should be able to read and understand scientific papers  should be able to employ gathered information to formulate and prove their own hypotheses within the relevant context.
 Syllabus: Laboratory of Parallel and Distributed Systems (ParaDiSe) is a team project focused on the development of parallel methods and tools for the design and analysis of complex systems. Students meet regularly with senior researchers to discuss research problems related to their research topics.
IV100 Parallel and distributed computations
zk 2/0 2 kr., podzim
 prof. RNDr. Rastislav Královič, Ph.D.
 Prerequisities:
IB002
IB002 (Design of algorithms), required. PB152 (Operating systems) recommended.  Goals: The aim of the course is to introduce students into the field of distributed computation. It presents the basic concepts, problems and solutions. Algorithms for selected group of problems give the insight into the techniques used in the field and show how the various environments influence the quality and (un)solvability of the problem.
 Learning outcomes: Students will know particular algorithms for distributed routing, leader election and termination.
 Syllabus:
Distributed systems and distributed algorithms.
Communication protocols. Alternatingbit protocol, slidingwindow protocol.
Routing algorithms. Routing tables and algorithms for their constructions. FloydWarshallův algorithm, shortestpath algorithm.
Distributed mutual exclusion. Distributed election algorithms. Ring networks and a general topology. Impact of synchrony. Impact of sense of direction.
Termination detection. DijkstraScholten algorithm.
The problem of Byzantine generals and its (un)solvability in various environments.
IV105 Bionformatics seminar
k 0/1 1 kr., podzim
 Mgr. Monika Čechová, Ph.D.  Ing. Matej Lexa, Ph.D.
 Prerequisities: Those who sign up for this interdisciplinary course should be able to read and comprehend a scientific paper or book chapter written in English. Alternatively computational tools in bioinformatics will be studied (deeper knowledge of algorithm design and programming will allow the particular student to focus more on the biological side of the studied problems or vice versa). Students of nonbiological fields should be concurrently enrolled in, or have previously passed IV107 Bioinformatics I. Alternatively they may frequent the course with the consent of the teacher.
 Goals: A bioinformatics course opening up the world of genes and molecules to students via external lecturers and student presentations organized as a journal club.
 Learning outcomes: Students will gain insight into problems studied in bioinformatics; they will practice presentation and discussion techniques in front of an audience.
 Syllabus:
The students will chose publications to study recent methods in genomic sequence analysis (using suggested journal articles or other material approved by the teacher) covering
Nanopore DNA sequencing methods
Tools and algorithms for processing and analysis of long/nanopore reads
Genomic and biological studies based on nanopore sequencing
IV106 Bioinformatics seminar
k 0/1 1 kr., jaro
 Mgr. Monika Čechová, Ph.D.  Ing. Matej Lexa, Ph.D.
 Prerequisities: Those who sign up for this interdisciplinary course should be able to listen to lectures and to read and comprehend a scientific paper or book chapter written in English. Deeper knowledge of algorithm design and programming will allow the particular student to focus more on the biological side of the studied problems or vice versa. Students of nonbiological fields should be concurrently enrolled in, or have previously passed IV107 Bioinformatics I. Alternatively they may frequent the course with the consent of the teacher.
 Goals: Longterm the seminar covers "Biological (molecular) and biomedical data analysis". Individual runs may focus on a specific subtopic.
 Learning outcomes: Students will gain insight into problems studied in bioinformatics; they will practice presentation and discussion techniques in front of an audience.
 Syllabus:  Introduction to deep learning in bioinformatics  Lectures of invited lecturers covering the same topic  Students will chose a publication from this area to present in class in a 'journal club' format
IV107 Bioinformatics I
zk 2/1 2 kr., podzim
 Ing. Matej Lexa, Ph.D.
 Prerequisities: This is an entry course into the area of bioinformatics for students of nonbiological disciplines, there are no prerequisites.
 Goals: This course will lead the students into the fascinating world of molecules, genes and proteins. Currently, bioinformatics is going through a period of unusual growth. Abilities to think and act as a bioinformatician (to work with large biological datasets using modern computer science methods) are needed in many areas of science and applied disciplines, especially biology, medicine and chemistry.
 Learning outcomes: After taking the course, the students will understand basic principles of molecular biology; they will be familiar with important biological problems that can be best handled by computers; they will understand and be able to choose basic computational methods for handling molecular data.
 Syllabus:
The history and subject of bioinformatics
Basics of molecular biology
Organization of living matter
DNA structure and function
Protein structure and function
Evolution of genes and proteins
Bioinformatic data
Data sources
Common data types
Public sequence data and their accessibility
DNA sequence analysis
Computer exercises: Data sources, similarity search, visualization of molecules
Protein sequence analysis
Structural and functional data
Similarity searches and scoring
Other types of data and their analysis
Expression data
Protein digests and mass spectra
Literature data analysis
IV108 Bioinformatics II
zk 1/1 2 kr., podzim
 Ing. Matej Lexa, Ph.D.
 Prerequisities: IV107 Bioinformatics I or consent of the teacher (not needed for biology students).
 Goals: Introduction to selected algorithms and methods of analysis used in bioinformatics.
 Learning outcomes:
At the end of the course, the students will:
understand the inner workings of selected algorithms, their advantages and disadvanteges, including knowledge of recent alternatives
be able to work with 3D models of molecules
be able to evaluate or design methods for solving current problems in bioinformatics
understand the principles of existing DNA sequencing methods and processing sequencing data  Syllabus:
Algorithms for sequence analysis
Algorithms for prediction and analysis of structural data
Biological language
Nextgeneration DNA sequencing methods and data processing
Understanding protein cleavage and mass spectra
Expression profile and promoter analysis
IV109 Modeling and Simulation
zk 2/1 3 kr., jaro
 doc. Mgr. Radek Pelánek, Ph.D.
 Goals: The course offers a wide overview of computational modeling and gives students a practical experience with computational modeling.
 Learning outcomes: At the end of the course students will be able to: describe main concepts of complex systems (particularly "feedback loops"); explain main principles and applications of computational modeling; compare modeling approaches; describe wellknow case studies in computational modeling; create a computational model.
 Syllabus:
Introduction, history, role of modeling and simulation in research,
applications. Computational models.
Complex systems, system thinking, feedback loops.
System dynamics approach, examples (demographics, Limits to growth).
Agent based modeling: basic principles, cellular automata, decentralized systems.
Game theory, models of cooperation. Models of adaptation (genetic algorithms, neural networks).
Modeling of networks: examples of networks and their properties, models of networks.
Analysis and evaluation of models.
Application of modeling from different areas (e.g. economics, traffic, epidemiology, biology).
IV110 Bioinformatics project I
k 1/1 2 kr., podzim
 Mgr. Monika Čechová, Ph.D.  Ing. Matej Lexa, Ph.D.
 Prerequisities: IV107 Bioinformatics I plus elementary programming skills (e.g. UNIX + C/C++/Java + Perl/Python) or teacher's consent
 Goals:
In this course the students will:
be able to select appropriate bioinformatic tools for a given problem
be able to carry out independent analysis of bioinformatic data
present their results to their colleagues  Learning outcomes:
In this course the students will:
be able to select appropriate bioinformatic tools for a given problem
be able to carry out independent analysis of bioinformatic data
present their results to their colleagues  Syllabus:
Discussion of interesting problems to solve
Preparation of student proposals
Programming phase
Student miniconference
IV111 Probability in Computer Science
zk 2/2 3 kr., podzim
 doc. RNDr. Vojtěch Řehák, Ph.D.
 Prerequisities: Knowledge of basic discrete mathematics (e.g. as presented in the course IB000).
 Goals: At the end of the course student should have a broad knowledge and an ability of independent study of problems based on the probability theory and its computer science applications. Will be able to apply the results of the probability theory in practical examples. Should be able to learn independently new problems requiring knowledge of probability theory. Will be able to characterise basic principles of data compression and error correction. Should be able to apply information theory results in practice.
 Learning outcomes: Student is able: to define basic terms of the mentioned topics (e.g., random variable, expectation, variance, random process, Markov chain, channel capacity, code rate); to explain meaning on the terms on practical examples; to solve simple examples e.g. using linearity o expectation; to provide basic analysis on both discrete and continuoustime Markov chains; to compute (conditional) expectation, mutual information, and entropy random variables with given probability distribution; to demonstrate basic proof mentioned during lectures.
 Syllabus:
Probability. Discrete probabilistic space.
Random variable and its applications. Expectation and variation.
Markov and Chebyshev inequalities. Chernoff bounds. Weak and strong law of large numbers.
Random processes. Markov processes.
Entropy. Information.
Applications in computer science (information theory, coding theory, cryptography etc).
IV114 Bioinformatics and Systems Biology Project
k 0/1 2 kr., podzim
 Mgr. Monika Čechová, Ph.D.  Ing. Matej Lexa, Ph.D.
 Prerequisities: The students should have finished IV107 Bioinformatics I, be acquainted with NGS/sequencing data processing tools and have elementary programming skills in any programming language/environment (optimally UNIX with C/C++/Java and Perl/Python) or consent of the lecturer
 Goals:
In this course the students will:
get acquainted with DNA nanopore sequencing
be able to select appropriate bioinformatic tools for a given problem
be able to carry out independent analysis of bioinformatic data
present their results to their colleagues  Learning outcomes:
In this course the students will:
get acquainted with DNA nanopore sequencing
be able to select appropriate bioinformatic tools for a given problem
be able to carry out independent analysis of bioinformatic data
present their results to their colleagues  Syllabus:
Familiarization with DNA sequencing using minION technology
Preparation of student proposals in pairs (selection of material for sequencing; preparation of bioinformatic tools and pipelines)
Analysis phase (DNA sequencing and data collection; data processing by filtration, mapping and assembly; visualization)
Student miniconference and optional participation in paper writing (depending on quality of results)
IV115 Parallel and Distributed Laboratory Seminar
z 0/2 2 kr., podzim
 prof. RNDr. Jiří Barnat, Ph.D.
 Prerequisities:
souhlas
Ability of selfeducation by reading latest scientific papers focused on modeling and verification of complex systems.  Goals: Students acquire experience with preparing presentations of their own research work and should be able to actively participate in research activities of the ParaDiSe laboratory.
 Learning outcomes: Experience with presentation of research results to adequately educated audience.
 Syllabus: Discussion topics and papers to be studied and presented are specified during the first two weeks of semester.
IV119 Seminar on Discrete Mathematical Methods
k 0/2 2 kr., jaro
 prof. RNDr. Petr Hliněný, Ph.D.  prof. RNDr. Daniel Kráľ, Ph.D., DSc.
 Prerequisities: Basics of undergraduate mathematics (IB000 is enough).
 Goals: The aim of this seminar is to introduce interested students into the beauties of mathematics and of clean mathematical proofs. This will teach students "mathematical thinking"  to understand math definitions, statements, and proofs in their full depth, and to make their own new proofs in all areas of mathematics and theoretical computer science.
 Learning outcomes: After finishing this seminar, successful students should be able to understand presented mathematical proofs in their full depth, and to make their own new proofs in areas of mathematics and theoretical computer science.
 Syllabus:
Selected nice topics from "Proofs from THE BOOK"; TBA each year.
Number theory, Combinatorics, Combinatorial geometry, Graph theory.
IV121 Computer science applications in biology
zk 2/1 3 kr., jaro
 Ing. Matej Lexa, Ph.D.  Mgr. Bc. Vít Nováček, PhD  doc. RNDr. David Šafránek, Ph.D.
 Prerequisities: The course has no specific initial requirements. The goal is to inform students and young researchers of life sciences about several opportunities to apply computer scientific techniques in their field.
 Goals:
The main aim of the course is to bridge the gaps between computer science and life sciences by presenting the selected theoretical problems solved in computer science
After absolving the course students should be able to:
understand selected parts of computer science that make a tool for life sciences;
get a comprehensive overview of computational tools and techniques relevant for life sciences;
get basic knowledge of selected software tools (ability to execute programs and to use their elementary features).  Learning outcomes:
After absolving the course students should be able to:
define the contribution of computer science for biology and biomedicine;
associate relevant tools and techniques with selected set of biological problems;
use selected tools at basic level and apply them to simple models.  Syllabus:
1. Introduction to bioinformatics and systems biology
2. Qualitative models and their analysis
3. Quantitative models and their analysis
4. Networkbased representation of information
5. Data mining from literature
6. Data integration
7. Applications of artificial intelligence and machine learning techniques
8. 3D geometry, CSG
IV123 InformaticsDriven Future
zk 2/0 2 kr., jaro
 prof. RNDr. Jozef Gruska, DrSc.
 Prerequisities: There are no special technical requirements. Main requirement is a deeper interest to know the expected role of Informatics for society in future, , as well as its main challenges and potential
 Goals: Exponentially fast developments in Informatics, especially in information storing, transmission and processing driven technologies, and in artificial intelligence, create potential for enormous impacts on society. The impact that has potential to be very positive, but also very negative, even historical. Moreover, due to that development, what can be nowadays expected as to happen in the next 50100 years, in most of the areas of society, especially in science, technology, health care,...., if the current rate of development is sustained, can happen actually already within next 2040 years. The goal of the course is to provide a visionary and thoughtsprovoking, but well grounded, analysis of the main developments that we can, reasonably, expect, and why, in the (very) near future. Especially due to the development in all information processing and communication driven technologies, nanotechnologies, genetics, nonbiological (artificial) intelligence and in fights with natural death and in explorig intelligence as a commodity. Informatics, once properly understood and developed%and sufficiently broadly and deeply understood, is to play at that a key role. Merits of the favorable future, but also ways to avoid perils, if possible, will also be discussed. The course should be of interest and importance to all those interested to find out the frameworks, tools, tasks and main challenges they and society will face in the (already quite near) future. To understand that should be for anyone not only very interesting, but actually much needed for knowing how to prepare oneself in the best way for the expected long future carrier in enormously fast changing frameworks. Contents: 1. Introduction: Why and how to foresee future? Main megachallenges. 2. Evolution  from biological to nonbiological one and to their merge. 3. Exponential acceleration of all informationdriven technologies. 4. New perception of Scientific Informatics and its grand challenges. 5. Impulses and roads to a new perception of Informatics 6. Technological and Applied Informatics and their grand challenges. 7. New, Informaticsdriven, methodology and its grand challenges. 8. Developments in the understanding and simulation of human brains. 9. GNRrevolution  Artificial intelligence and robotics,aibeings 10. GNR revolution  Genetics and Nanotechnologies. 11. Singularity: merge of bio and nonbiointelligencemerits/perils. 12. Longevity  Can we fight death? Can we make life enjoyable till/after 150?!
 Learning outcomes: Understanding of unpredictability of impact of IT evolution to the future.
 Syllabus: 1. Introduction. Why and how we need/can forsee future? Old and new megachallenges of science, technology and informatics. 2.Evolution. From biological to nonbiological (technological) evolution and their merge. Enormous expected impacts of the merge ot the biological and nonbiological intelligence. 3. Exponential developments in information processing and communication technologies and their impacts on science, technology and the rest of society 4.New perception of informatics and its grand challenges. Informatics as a merge of scientific, engineering, methodological and application informatics. New perception of the scientific informatics and its grand challenges. 5. New perception of the technological iand applied nformatics and their grand challenges. 6. New, informaticsdriven methodology for science, technology, and actually for all areas of society. 7. Recent developments in understanding and simulation of human brains and minds 8. GNR revolution  I. Robotics and artificial intelligence. Long hhistory, hot outcomes, big dangers. 9. GNR Revolution  II. Geniomics and nanotechnologies 10. Singularity  a merge of biological and nonbiological evolution. 12. Longevity  Can we fight death? How (much). Can we make life ebjyable till/after 150?!
IV124 Complex Networks
k 0/2 2 kr., jaro
 doc. RNDr. Eva Hladká, Ph.D.  Ing. Eva Výtvarová
 Goals: Many complex systems can be viewed as a network of interacting units. This view helps to study and understand important phenomena present in systems such as human brain, internet, economy, social groups and others. The prezence of big data stimulated development of fundamental theories to describe and analyse complex networks in many fields. Course includes tutorials where real data sets will be analysed.
 Learning outcomes: The students will be able to define the local and global network topological measures, and will be aware of the algorithms which can be used for their computation. They will be ready to explain the principles of generating random graphs with given structural properties and to understand their role in data analysis. The students will be able to apply the complex network analysis to empirical data sets across the application domains. Given raw data, they will be also capable of designing a networkoriented analysis, formulate relevant hypothesis and interpret correctly the results.
 Syllabus:
Intro
Random graphs
Central nodes
COmunity structure
Application of centrality and modularity
Power law I
powerlaw II
Smallworld networks
Random walks
Robustness and stability
Socialeconomic networks
Internet as a complex network
Biologic networks
Visualisation
IV125 Formela lab seminar
k 0/2 2 kr., podzim
 prof. RNDr. Antonín Kučera, Ph.D.  doc. RNDr. Tomáš Brázdil, Ph.D.  doc. RNDr. Vojtěch Řehák, Ph.D.  doc. Mgr. Jan Obdržálek, PhD.  RNDr. Petr Novotný, Ph.D.  Dr. rer. nat. Achim Blumensath
 Prerequisities:
souhlas
The enrollment must be approved by a teacher of a seminar group.  Goals: By passing this course, the student will gain a profound insight into the relevant part of theoretical computer science or mathematics (depending on the selected seminar group), will be able to read and present results formulated in a given scientific literature.
 Learning outcomes: The student has a profound insight into the relevant parts of theoretical computer science or mathematics (depending on the chosen seminar group), is able to present the given specialized substance independently and solve related research problems.
 Syllabus:
Team study or team research project.
At the beginning of the semester the studied topics is selected in a common discussion.
At regular weekly seminars a selected student (or the lecturer) explains the subject.
At the designated seminars students refer to their progress and results, they are given feedback.
At the final seminar an overall assessment is made.
IV125 Formela lab seminar
k 0/2 2 kr., jaro
 Dr. rer. nat. Achim Blumensath  doc. RNDr. Tomáš Brázdil, Ph.D.  prof. RNDr. Antonín Kučera, Ph.D.  RNDr. Petr Novotný, Ph.D.  doc. Mgr. Jan Obdržálek, PhD.  doc. RNDr. Vojtěch Řehák, Ph.D.
 Prerequisities:
souhlas
The enrollment must be approved by a teacher of a seminar group.  Goals: By passing this course, the student will gain a profound insight into the relevant part of theoretical computer science or mathematics (depending on the selected seminar group), will be able to read and present results formulated in a given scientific literature.
 Learning outcomes: The student has a profound insight into the relevant parts of theoretical computer science or mathematics (depending on the chosen seminar group), is able to present the given specialized substance independently and solve related research problems.
 Syllabus:
Team study or team research project.
At the beginning of the semester the studied topics is selected in a common discussion.
At regular weekly seminars a selected student (or the lecturer) explains the subject.
At the designated seminars students refer to their progress and results, they are given feedback.
At the final seminar an overall assessment is made.
IV126 Fundamentals of Artificial Intelligence
zk 2/0 3 kr., podzim
 doc. Mgr. Hana Rudová, Ph.D.
 Prerequisities:
The course is a continuation of the PB016 Introduction to Artificial Intelligence, PB016 completion is not a prerequisite for course completion.
It is presumed knowledge of probability theory corresponding to the course MB103 Continuous models and statistics.  Goals: The course completes comprehensive introductory knowledge of artificial intelligence following the course PB016 Artificial Intelligence I. The course discusses search algorithms concentrating on metaheuristics and local search, classical planning, uncertain reasoning, and introduction into robotics oriented on robot path planning.
 Learning outcomes:
The graduate will be aware of local search and metaheuristics algorithms and will be able to solve practical problems with their help.
The graduate will understand problematics of the AI planning, will learn how to represent planning problem and how to solve it using base algorithms.
The graduate will gain an overview of how to work with uncertainties in the given problem and will learn to use basic procedures for including uncertainty in problem solving.
The graduate will be aware of the base concepts from robotics which is used for demonstration how the above knowledge can be applied, especially in the planning of robot motion.  Syllabus:
Local search and metaheuristics:
Singlesolution based search, principles, and concepts, strategies for improving local search. Populationbased search, evolutionary algorithms, swarm intelligence.
Planning: Problem representation. State space planning, forward and backward planning, domainspecific planning. Plan space planning, partial order planning.
Uncertain knowledge and reasoning: Probabilistic reasoning, Bayesian networks, exact and approximate inference. Time and uncertainty. Utility theory, decision networks. Sequential decision problems, Markov decision processes.
Robotics: Robot hardware, robotic perception, robot scheduling in manufacturing. Path planning in robotics, movement.
IV127 Adaptive Learning Seminar
k 0/2 2 kr., podzim
 doc. Mgr. Radek Pelánek, Ph.D.
 Prerequisities:
SOUHLAS
 Goals: Students gain experience with reading and presenting research papers and they perform and present their own research and development in the area of adaptive learning.
 Learning outcomes: At the end of the course, students will be able to critically assess current research and development in the area of adaptive learning.
 Syllabus: Presentation of research papers from research areas relevant to the Adaptive learning lab  educational data mining, machine learning, cognitive psychology. Development of educational systems, analysis of educational data, presentation and discussion of results.
IV127 Adaptive Learning Seminar
k 0/2 2 kr., jaro
 doc. Mgr. Radek Pelánek, Ph.D.
 Prerequisities:
SOUHLAS
 Goals: Students gain experience with reading and presenting research papers and they perform and present their own research and development in the area of adaptive learning.
 Learning outcomes: At the end of the course, students will be able to critically assess current research and development in the area of adaptive learning.
 Syllabus: Presentation of research papers from research areas relevant to the Adaptive learning lab  educational data mining, machine learning, cognitive psychology. Development of educational systems, analysis of educational data, presentation and discussion of results.
IV128 Online Communication from Social Science Perspective
z 0/2 4 kr., jaro
 prof. PhDr. David Šmahel, Ph.D.
 Goals: This seminar should introduce basic theories and research of online communication, what will help students of informatics to better understand to specifics of online communication.
 Learning outcomes:  knowledge of basic theories of online communication  knowledge of theories related to experimenting with online identity  knowledge of theories and empirical research related to trust in online information
 Syllabus: 1. Intro 2. Communication theory 3. Reliability of online data 4. Privacy in virtual world 5. Phishing 6. A methodology of online communication 7. Communication in virtual teams. 8. Addiction to online communication 9. Health and internet 10. Mediation of using internet 11. Safe online behavior 12. Research in online security 13. Conclusions
IV129 Sybila Laboratory
z 0/0 2 kr., podzim
 doc. RNDr. David Šafránek, Ph.D.  prof. RNDr. Luboš Brim, CSc.
 Prerequisities:
SOUHLAS
Applicants should 1) be able to work independently 2) have interest in longterm projects (several semesters) 3) have working knowledge of English 4) be able to work in a team. The enrollment must be approved by one of the laboratory researchers (L. Brim, D. Šafránek).  Goals: On successful completion of the course students  will have practical experience with active research  should be able to read and understand scientific papers  should be able to employ gathered information to formulate and prove their own hypotheses within the relevant context  should be able to present the research results in English.
 Learning outcomes:
After the course the student will be able:
 to analyse and discuss pros and cons of computational methods in systems biology;
 to contrast conceptual errors in analysed publications by employing suitable arguments;
 to judge ideas and opinions in front of students and professionals.  Syllabus: Sybila Laboratory is a team project focused on the development of methods and tools for the design and analysis of biological systems and their experimental application. Students meet regularly with senior researchers to discuss research problems related to their research topics. Students are expected to regularly present stateoftheart results in the relevant fields at the laboratory seminar.
IV129 Sybila Laboratory
z 0/0 2 kr., jaro
 doc. RNDr. David Šafránek, Ph.D.  prof. RNDr. Luboš Brim, CSc.
 Prerequisities:
SOUHLAS
Applicants should 1) be able to work independently 2) have interest in longterm projects (several semesters) 3) have working knowledge of English 4) be able to work in a team. The enrollment must be approved by one of the laboratory researchers (L. Brim, D. Šafránek).  Goals: On successful completion of the course students  will have practical experience with active research  should be able to read and understand scientific papers  should be able to employ gathered information to formulate and prove their own hypotheses within the relevant context  should be able to present the research results in English.
 Learning outcomes:
After the course the student will be able:
 to analyse and discuss pros and cons of computational methods in systems biology;
 to contrast conceptual errors in analysed publications by employing suitable arguments;
 to judge ideas and opinions in front of students and professionals.  Syllabus: Sybila Laboratory is a team project focused on the development of methods and tools for the design and analysis of biological systems and their experimental application. Students meet regularly with senior researchers to discuss research problems related to their research topics. Students are expected to regularly present stateoftheart results in the relevant fields at the laboratory seminar.
IV130 Pros and Cons of Intelligent Systems
zk 2/0 2 kr., jaro
 prof. RNDr. Jiří Zlatuška, CSc.
 Prerequisities: No formal prerequisities.
 Goals: It is a course with a motivationalmethodologicalparadigmatic mission to open students' thinking towards the coming era of massive use of artificial intelligence (AI) in all aspects. It is about the link between intelligence in the general sense and the design of generalpurpose information processing machines (computers) with implications concerning the replacement of human cognitive activities by intelligently acting machines both in the position of cooperating with humans and as their adversaries, including implications for the cybersecurity of systems deployed so far (in both mentioned positions), but secure systems building on AI and avoiding the conflict between the goals pursued by the machine and the interests corresponding to humans. This includes fundamental ethical issues, including implications for security policies (from local to geopolitical). It broadens the technicaloperational perspective of the computersecurity expert, in the ability to perceive a forwardlooking view and be aware of the threats and opportunities that come with AI in the world of cybersecurity. The ambition of the course is not necessarily to teach the techniques directly used, but rather to provide a mental preparation for a world where opportunities are yet to be born, to see these techniques in the context of their evolution, status, potential, and with them, the definition of the role of AI with the broad implications that will come from these techniques, or techniques that will complement/overlay them..
 Learning outcomes: Upon successful completion, the student will:  Know and be able to describe basic concepts such as cognitive processes, memory functioning, mental and machine processes in decision making, the role of emotions  Know and understand the principles of Bayesian probability, Markov processes  Able to identify and describe the main influences of AI on cybersecurity threats and in turn on protection methods  Can apply techniques using Bayesian networks to describe and solve simple problems relevant to cybersecurity  Be able to describe and explain the concepts of utility, ethical aspects of AI  Be able to describe the main cybersecurity aspects of AIbased systems  To be familiar with the main problems of the relationship between future "real" (strong) AI and humans  Know the framework of legal regulations today and expected future regulating the field of AI  Be able to describe the known grand challenges associated with AI
 Syllabus:  Intelligence in people and machines, intelligence, memory, computation, learning  Possibilities and limits of machines, biology and evolution  Agents, environment, intentions, making and implementing plans  Environmental certainty, uncertainty, probability, game theory  Inference, logical systems, probabilistic languages, Bayesian networks  Intelligent systems development, learning methods, assisted learning, feedback systems, deep learning, explanationbased learning  The prospect of superintelligence, implications, fiction or danger  AI risks, breakthroughs, bugs vs. robust systems, surveillance, mental safety, autonomous weapons, human labor substitution, economic impacts  Impacts of superintelligent AI, the Gorilla problem and the King Midas problem, intelligence explosion, control problem  Coexistence of intelligent systems and humans, principle of beneficial machines, friendly AI  Mathematical principles of beneficial machine formulation, assistive games, machine shutdown problem  Preferences and values, their uncertainty, detection and adaptation, psychology and technology  Implications for future developments, big data policies, privacy, secure development of intelligent applications
IV131 Seminar of Discrete Methods and Algorithms Laboratory
z 0/0 2 kr., podzim
 prof. RNDr. Daniel Kráľ, Ph.D., DSc.  prof. RNDr. Petr Hliněný, Ph.D.
 Prerequisities:
SOUHLAS
The enrollment must be approved by one of the heads of the laboratory.  Goals: After the successful completion of the course students will have  experience with independent work with scientific text  ability to use the acquired knowledge and information to formulate and prove their own statements  skills to present the research results in English.
 Learning outcomes: Upon completion, the student will be have a deep knowledge of a selected topic concerning discrete algorithms or discrete mathematics.
 Syllabus: Selfstudy of scientific publications and their presentation to other members of the laboratory, analysis of possible approaches to improve the presented results.
IV131 Seminar of Discrete Methods and Algorithms Laboratory
z 0/0 2 kr., jaro
 prof. RNDr. Daniel Kráľ, Ph.D., DSc.  prof. RNDr. Petr Hliněný, Ph.D.
 Prerequisities:
SOUHLAS
The enrollment must be approved by one of the heads of the laboratory.  Goals: After the successful completion of the course students will have  experience with independent work with scientific text  ability to use the acquired knowledge and information to formulate and prove their own statements  skills to present the research results in English.
 Learning outcomes: Upon completion, the student will be have a deep knowledge of a selected topic concerning discrete algorithms or discrete mathematics.
 Syllabus: Selfstudy of scientific publications and their presentation to other members of the laboratory, analysis of possible approaches to improve the presented results.
PB001 Introduction to Information Technologies
zk 2/0 2 kr., podzim
 prof. RNDr. Luděk Matyska, CSc.  doc. RNDr. Eva Hladká, Ph.D.
 Prerequisities:
! PB002 && ! NOW ( PB002 )
 Goals:
To provide basic orientation in the information technology fields is the main goal of this lecture. Not only hardware and software is discussed, but the social extent is covered as well.
The graduate will be able to understand function of simple computer systems.
Graduate will be also able to understand and explain relationship between components of more complex computer systems.
Graduate will be also able to analyze and explain behavior of operating systems, computer networks etc. from the user point of view.
Graduate will be able to analyze and evaluate ethical implications of his own work.  Learning outcomes:
At the end of this course student will have basic orientation in computer and information technologies and their design. Specifically, she will understand basic principles of computer and computer systems design, she will be acquainted with principles of programming and specific constructs like interruption, synchronization, input/output. She will know basic principles of design of operating systems that are used as an example of complex computer systems; she will know what API is.
He will also get basic overview and orientation in computer networks an computer graphics.
The graduate will also get basic orientation in ethics and professional context and she will also be able to comment societal impacts of computer technology.  Syllabus:
Computer and communication system, role of components (architecture,
operating systems, computer networks), applications.
Social and ethical dimension of IT.
Computer architecture, data representation, von Neumann model, basic machine organization.
Role of operating systems, history, functionality of a typical contemporary OS.
Design principles, efficiency, robustness, flexibility, compatibility, ...
Influence of requirements of security, networking, graphical interfaces, ...
OS structure (monolithic, layered, modular, microkernel).
Abstraction, processes, resources, application interfaces.
I/O devices and their management, drivers.
Protection, system and user space, kernel.
Networks, their history, Internet, fundamentals of network architecture, distributed systems.
Protocols, multimedia systems, distributed computing, mobile and wireless computing.
Basics of HumanComputer interaction, graphical systems.
Social context of IT, Information society, New economics.
Internet, grows, management, international issues.
Professional and ethical responsibility, basic laws (personal data protections, digital signature, ...). Ethic codes, professional bodies, organizational's "Acceptable use policy".
PB006 Principles of Programming Languages and OOP
zk 2/0 2 kr., podzim
 doc. Mgr. Jan Obdržálek, PhD.
 Prerequisities:
( IB111  IB002 ) && PB071
The course is, due to its synthetic nature, intended for students with some knowledge of a modern imperative programming language (Python), some lowlevel language (C) and a functional programming language (Haskell).  Goals: The goal of this course is to introduce students to the various concepts present in modern programming languages. The course also serves as an introduction to objectoriented programming.
 Learning outcomes:
By the end of the course, the student will:
know all basic concepts present in modern programming languages;
be able to quickly obtain an indepth understanding a of new programming language and start using it;
understand the principles of objectoriented programming;
able to assess suitability of a given programming language for a particular problem domain;
to analyze both strong and weak aspects of a given programming language,  Syllabus:
Names, bindings, scopes. Block structure. Representing object in memory.
Primitive and composite types. Typechecking. Lists. Pointers and references. Type derivation.
Control flow: expressions, statements, control structures.
Subprograms: local definitions, parameter passing, overloaded and generic functions. Coroutines.
Abstract data types: basic concepts, encapsulation, case studies. Parameterized ADT, generics. Namespaces.
Object oriented programming: principles, inheritance, dynamic binding.
Exceptions: basic concepts, case studies, exceptions in functional programming languages.
Support for name handling and encapsulation in large programs.
PB007 Software Engineering I
zk 2/2 3 kr., podzim
 doc. Ing. RNDr. Barbora Bühnová, Ph.D.
 Prerequisities:
PB006  NOW ( PB006 )  PB161  NOW ( PB161 )  PB162  NOW ( PB162 )  IB114
 Goals: The aim of the course is to introduce the students to the lifecycle of the analysis, design, development and operation of complex software systems, respecting various aspects of systems quality and sustainability.
 Learning outcomes:
At the end of the course students should:
know the basic steps and tasks of the IS development process;
have detailed understanding of the techniques of requirements specification, system analysis and design, testing and maintenance;
know the characteristics of objectoriented approach to system analysis and design;
be able to model a mediumsize IS in UML.  Syllabus:
Software development, UML Use Case diagram.
Requirements specification, UML Activity diagram.
System analysis and design, structured vs. objectoriented A&D.
Object oriented analysis, UML Class diagram, Object diagram and State diagram.
Data modelling and management, ERD.
Highlevel design, UML Class diagram in design.
Lowlevel design and implementation, UML Interaction diagrams.
Architecture design, UML Package, Component and Deployment diagram.
Testing, verification and validation.
Operation, maintenance and system evolution.
Software development management.
Advanced software engineering techniques.
PB009 Principles of Computer Graphics
zk 2/1 3 kr., jaro
 doc. RNDr. Barbora Kozlíková, Ph.D.  RNDr. Jan Byška, Ph.D.  RNDr. Katarína Furmanová, Ph.D.  Mgr. Marek Trtík, Ph.D.
 Prerequisities: The knowledge of matrix calculus, linear algebra, and geometry.
 Goals: The aim of the course is to give the students an overview of the basic algorithms and methods for modeling and rendering. The gained knowledge will enable the students to understand a vast range of computer graphics tasks, including their mathematical background. Within the seminars, students will implement selected basic algorithms explained within the lectures.
 Learning outcomes:
After finishing the course, the students
 will orient themselves in a broad spectrum of computer graphics problems;
 will understand the underlying math foundations and programming principles in computer graphics;
 gain practical knowledge in programming selected algorithms.  Syllabus:
Introduction to computer graphics and its application areas.
Basic graphical primitives and algorithms for rasterization, differences between raster and vector graphics.
Sampling, reconstruction, antialiasing, algorithms for closed area filling.
2D clipping.
Representation and modeling of 3D objects.
2D and 3D transformations, projection.
Freeform curves and surfaces.
Visibility in object space.
Speedup techniques for realtime rendering, scene hierarchy representations.
Color, color models, color perception.
Basic lighting models, shading, texturing.
Ray tracing, basics of photorealistic and physicallybased rendering.
PB016 Introduction to Artificial Intelligence
zk 2/2 3 kr., podzim
 doc. RNDr. Aleš Horák, Ph.D.  doc. RNDr. Lubomír Popelínský, Ph.D.
 Prerequisities: Basic knowledge of the Python programming language is expected, Python is used in the exercises.
 Goals: Introduction to problem solving in the area of artificial intelligence. The main aim of the course is to provide information about fundamental algorithms used in AI.
 Learning outcomes:
After studying the course, the students will be able to:
 identify and summarize tasks related to the field of artificial intelligence;
 compare and describe basic search space algorithms;
 compare and describe main aspects of logical systems;
 understand different approaches to machine learning;
 compare and describe different ways of knowledge representation and reasoning;
 present basic approaches to computer processing of natural languages.  Syllabus:
Artificial intelligence, Turing test, problem solving
Solving problems by searching.
Problem decomposition, AND/OR graphs, Constraint Satisfaction Problems.
Games and basic game strategies.
Logic agents, propositional logic, satisfiability.
Truth and provability. Axiomatic systems.
First order predicate logic, intensional logic.
Resolution in propositional and predicate logic. Introduction to logic programming.
Modal logic. Multivalued logic.
Knowledge representation and reasoning, reasoning with uncertainty.
Learning, decision trees, neural networks.
Natural language processing.
PB029 Electronic Document Preparation
zk 2/1 3 kr., podzim
 doc. RNDr. Petr Sojka, Ph.D.
 Prerequisities: No strict prerequisites are set. Knowledge of basic algorithmic techniques and attention to detail is a plus. Students would benefit from basic knowledge of Unix scripting environment (e.g. from attending PV004 UNIX).
 Goals: Basic principles, algorithms and techniques used in electronic publishing are taught. Point of view is narrowed with respect to the creation of sizable, structured, technical documents like theses or dissertation, and presentations  it is ideal to enroll before writing first thesis (in TeX). Web publishing is also covered.
 Learning outcomes:
At the end of the course students should be able to apply basic principles, algorithms and technologies of (team) document preparation and production, namely as an author of technical documents (thesis, course presentation materials, program documentation, web presentation or for quality database publication). Students will use typesetting system TeX suite of programs.
Students will be able to pick up and work with appropriate tools and software packages used in the electronic documents' development cycle and preparation of above specified document types.  Syllabus:
From author to the reader: document preparation cycle.
Markup Logical structure of a document; markup languages. LaTeX as a markup language. HTML5, SGML, HTML, XML. Document grammars. DTD, XML Schema.
Design. Principles of book design. Specifics of web design. CSS, XSL(T).
Typesetting. Typesetting, typography. Basic principles, rules for Czech and English. Terminology.
Typefaces. Classification of type. Formats, rasterization of fonts. Type 1, Multiple master fonts.
Typesetting systems. TeX  principles, philosophy, macro programming. Microtypography.
Algorithms of line and page breaking, hyphenation.
Page description languages. PostScript. Bézier curves.
Print and Distribution. Output devices and their characteristics. Phototypesetting, printing and binding.
Hypertext, hypertext systems. Formats for electronic delivery. Portable Document Format, technology Adobe Acrobat.
Parallel WWW and paper publication. Database publishing. Document format conversion. Specifics of presentation preparation.
PB050 Modelling and Prediction in Systems Biology
zk 1/1 2 kr., podzim
 doc. RNDr. David Šafránek, Ph.D.
 Prerequisities: This is an interdisciplinary course that extends the knowledge of bachelor students of all study branches. The course is especially recommended for students of Bioinformatics.
 Goals:
At the end of the course, students will be able to:
understand basic principles of quantitative modeling,
understand dynamic computational models of complex systems in the domain of biological processes;
apply abstract computerscientific thinking to modeling and analysis of complex systems with special focus to biological systems;
practically use stateoftheart modeling and analysis software tools;
model and analyze dynamic properties of complex interaction networks.  Learning outcomes:
At the end of the course, students will be able to:
describe basic principles of quantitative modeling,
constract dynamic computational models of complex systems in the domain of biological processes;
apply abstract computerscientific thinking to modeling and analysis of complex systems with special focus to biological systems;
use stateoftheart modeling and analysis software tools.  Syllabus:
History and scope of systems biology.
Basic notions: living organism as a system with precisely given structure and functionality, in silico model, abstraction, simulation and prediction, model validation.
Specification of a biological model: biological networks and pathways, languages SBML and SBGN.
Emergent properties of systems dynamics, their specification and encoding.
Modeling and simulation of biological systems dynamics: hypotheses prediction.
Modeling of Escherichia coli bacteria: genetic regulatory network, models of locomotion organ synthesis and chemotaxis, nutritional stress response models.
Notion of stochasticity in biological dynamics, basic principles of stochastic models, chemical master equation, Monte Carlo simulation.
Model parameters, robustness and parameter sensitivity.
PB051 Computational methods in Bioinformatics and Systems Biology
zk 1/1 2 kr., jaro
 Ing. Matej Lexa, Ph.D.  doc. RNDr. David Šafránek, Ph.D.  Mgr. Bc. Vít Nováček, PhD
 Prerequisities: Knowledge of elementary molecular biology and basic problems in bioinformatics and systems biology is expected. Previous undertaking of courses IV107, PA054 and PB050 is welcome, but not required formally.
 Goals: At the end of the course students should be able to: select appropriate methods for a given problem; obtain and prepare necessary data; analyse the data (using their own program or publically available solutions)
 Learning outcomes:
At the end of the course students should be able to:
 select appropriate computational methods for a given problem;
 analyse selected types of experimental data;
 apply software tools to selected problems of data processing;
 construct and modify qualitative models of biological networks.  Syllabus:
The course will be divided into two parts, each will cover 12 methods:
Bioinformatics:
operations on genomes
using Markov models
Systems biology:
static analysis of biological networks; Cytoscape, MAVisto tools
genetic regulatory networks reconstruction; Genomica, ARACNE tools
data integration
PB069 Desktop Application Development in C#/.NET
zk 2/2 3 kr., jaro
 RNDr. Jaroslav Pelikán, Ph.D.
 Prerequisities: Knowledge of programming, C# programming language that is adequate to the course PV178 Introduction to Development in C#/.NET, and MS Windows operating system (on a user level).
 Goals: The course Desktop Application Development in C#/.NET introduces students to the basic techniques used for development of applications aimed for the environment of MS Windows operating systems. The course targets development of simple applications together with their graphical user interface, using C# programming language, MS .NET platform, Windows Forms programming interface, and Windows Presentation Foundation. The topics are taught and demonstrated in Microsoft Visual Studio.
 Learning outcomes:
At the end of the course, the student will:
know the principles and rules of eventdriven programming;
be familiar with the MS Visual Studio environment, Windows Forms interface, and Windows Presentation Foundation;
know the rules of designing graphical user interfaces of applications;
be able to use the basic classes of the Windows Forms interface and the Windows Presentation Foundation;
be able to develop applications for the MS Windows environment together with their user interface.  Syllabus:
Event driven programming. The model of a MS Windows program.
Integrated development environment MS Visual Studio, simple applications creation.
Usage of Windows Forms basic classes and work with them.
Processing of events coming from the keyboard and mouse.
Programming with graphics.
Dialog boxes and their handling.
Basic standard dialogs.
Programming with the clipboard, support of drag & drop.
Rules for the design of graphical user interface of applications.
Design of applications using Windows Presentation Foundation.
XAML language.
Classes representing controls.
New WPF features.
Resources, styles, triggers.
Programming with graphics.
Data binding.
Templates.
Printing from the environment of MS Windows.
Support of multithreading in applications.
Programming with Windows registry.
PB071 Principles of lowlevel programming
zk 2/2 4 kr., jaro
 doc. RNDr. Petr Švenda, Ph.D.
 Prerequisities: Students are expected to have the basic knowledge of algorithmization in Python or another procedural language. Students are also required to have user experience with Unix/Linux OS, as homework is submitted and tested on a Unix/Linux server.
 Goals:
At the end of the course students should be able to:
Understand and use basic C syntax according to ANSI and ISO/IEC norms.
Decompose given problem and perform practical implementation.
Use modern development tools (IDE, debugger, version control...).
Understand basic C functions for POSIX system.
Annotate source code with possibility to automatically generate documentation. Understand and practically use documentation for existing functions.
Use and follow programming best practices.
Compile programs both under Unix/Linux and Windows.  Learning outcomes:
After a course completion, the student will be able to:
 write nontrivial programs in C language;
 use basic development tools including IDE, debugger, versioning system and dynamic analysis of memory use;
 understand code execution on the level of CPU and memory;
 use dynamic allocation and apply correct deallocation where necessary;
 write programs with an application of good programming practices;  Syllabus:
Historical background of the C language. Its relation to the Unix OS.
C compilers under Unix and MSDOS/MSWindows, integrated development environment, debugger, version control. Good programming practices, testing.
Data types, constants, declarations, expressions. Assignment expressions and statements.
Basic program structure. Preprocessor statements. Comments. Control structures. Relational expressions. Elementary I/O operations.
Arrays and pointers. Functions. Calling by value, passing arguments by pointer.
User defined data types. Dynamic memory allocation.
I/O in details. Using files. Wide characters.
Strings and string manipulation. Standard C library according to ANSI and ISO/IEC standards. Calling Unix core services. Further Unix libraries for C. POSIX C Library. Implementation on Windows.
Safe and defensive programming.
Automated and manual testing.
PB095 Introduction to Speech Processing
zk 2/0 2 kr., podzim
 Mgr. Luděk Bártek, Ph.D.
 Goals: The course provides an introduction to speech processing oriented to humancomputer interaction, i.e. especially to speech synthesis, speech recognition and dialogue systems. Main objectives can be summarized as follows: To understand the basic principles of sound and speech production and perception; To understand basic principles of speech regognition,synthesis and dialogue systems; To obtain an introductory overview in the field.
 Learning outcomes: Student will be able after finishing the course to describe and explain the basic terms, methods and standards in following areas:
 physical acoustics
 physiological acoustics, especially the processes of forming and understanding the human speech
 phonetics and phonology
 signal digitization and basic signal processing in time and frequency domains
 isolated words and commands recognition
 continues speech recognition
 time and frequency domain texttospeech synthesis
 relation of prosody a emotions to tts and speech recognition
 dialogue communication
 dialogue systems
 user modeling in dialogue systems
 dialogue systems applicaiton.
 Syllabus:
Introduction
Brief history
State of the art
Physical and physiological acoustics
Creation and perception of human speech
Phonetics a phonology
Signal processing
Principles of speech synthesis
Speech segments and concantenative speech synthesis
Prosody, emotions
Principles of speech recognition
Statistical approaches
Modelling by means of HMM
Language modelling
Humanhuman and humancomputer communication
Dialogue
Dialogue Systems  Voice Browser Activity Standards (VoiceXML, SRGS, SISR, etc.)
User modelling
Dialogue systems and applications
PB106 Corpus Linguistic Project I
z 0/2 2 kr., podzim
 doc. Mgr. Pavel Rychlý, Ph.D.
 Goals:
The aim of the seminar is to provide students with a deeper knowledge
concerning a chosen area of corpus linguistics and practical checking
of this knowledge by working on the project. The popularisation of
corpus linguistics and other areas of language engineering is one of
the main goals of Natural Language Processing Centre at the
Faculty of Informatics.
Fundamental information about the Natural Language Processing Centre and corpus linguistics in general can be found at http://nlp.fi.muni.cz/.  Learning outcomes: Student will be able to: create a text corpus from different sources; use automatic tools for corpus annotation or information extraction; evaluate accuracy of automatic tools; present evaluation results.
 Syllabus:
theme introduction: text corpora, parallel corpora, annotation, statistics, user interfaces
project selection
work on a project
presentation of project results and discussion
PB130 Introduction to Digital Image Processing
zk 2/1 3 kr., podzim
 doc. RNDr. Petr Matula, Ph.D.
 Prerequisities:
! PV131
Knowledge of highschool mathematics and understanding and design of simple algorithms is supposed.  Goals: The objective of the course is to introduce students to the area of digital image processing in order to get necessary background for studying other courses from the area.
 Learning outcomes: At the end of the course a student should: know the basic terminology related to digital image processing; know about the typical problems from digital image processing; understand the principle of simple algorithms for image processing and know how to use them. The course is intended as an introduction to digital image processing.
 Syllabus:
Human vision, image acquisition and basic image computer representations.
Color images.
Point transforms. Histogram.
Linear image filtering. Convolution.
Nonlinear filters.
Mathematical morphology.
Edge detection. Gradient.
Regions in binary images and their description.
Image segmentation.
Applications of digital image processing.
PB138 Modern Markup Languages and Their Applications
zk 2/2 3 kr., jaro
 doc. RNDr. Tomáš Pitner, Ph.D.  Mgr. Luděk Bártek, Ph.D.  Ing. Lukáš Grolig
 Prerequisities: Basic knowledge of formal languages, as well as some experience in OO programming, such as in Java, C++, C# or ECMAScript, and databases is required. Moreover, basic orientation in a markup language (HTML) and Internet services is needed, too.
 Goals: The subject acquaints with basic standards and principles of working with technologies of modern markup languages mainly based on XML, JSON and Yaml. Upon successful completion of this course, students will create modern applications written in TypeScript using React and modern markup languages that retrieve data from the backend using REST interfaces or GraphQL query language, or using hybrid persistence, such as XML extensions of RDBMS or NoSQL databases.
 Learning outcomes:
Students will be able to:
Explain the advantages, disadvantages and purpose of tagged data and documents
Actively use terminology of markup languages
Choose the appropriate markup language for the application
Actively use XML
Describe the characteristics of Internet technologies such as HTML 5, JavaScript, React, JSON, GraphQL and other modern web standards and use them in real applications
Describe principles of Single Page applications  Syllabus:
Structure and terminology of marked documents.
XML: standards of the XML family, analysis and processing of XML data.
Document object model, eventdriven processing.
Navigation and querying in XML data. XPath, XML databases and extensions.
YAML: syntax and application
HTML documents
Basics of modern javascript applications, Node.js ecosystem, dependency management using NPM
Introduction to React, JSX/TSX language
Definition of components and their composition, life cycle of components, application status, work with lists, forms
Validation of web applications using Lighthouse JSON: syntax, data types, arrays, objects
Principles of the REST interface
GraphQL: schemas, queries, mutations, types, validation
PB150 ComputerSystems Architectures
k 2/0 2 kr., podzim