FI MU Study Catalogue 2019/2020

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Artificial Intelligence and Data Processing

follow-up 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 in-depth 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 so-called 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 start-up specializing in the use of artificial intelligence methods in a particular area.

Requirements for successful graduation

Compulsory subjects of the program

MA012 Statistics II
IV126 Artificial Intelligence II
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: 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 Bionformatics II
IV110 Bionformatics 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
PV082 Computational Chemistry
PV269 Advanced methods in bioinformatics
PV270 Biocomputing

Recommended course of study

Fall 2019 (1. term)
Spring 2020 (2. term)
Fall 2020 (3. term)
Spring 2021 (4. term)

Specialization: Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence specialization leads graduates to gain in-depth 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
PA153 Natural Language Processing
Logic and Algorithms Pass at least 3 courses of the following list
MA007 Mathematical Logic
PA164 Machine learning and natural language processing
IA008 Computational Logic
PA163 Constraint programming
IA168 Algorithmic game theory
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
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

Recommended course of study

Fall 2019 (1. term)
Spring 2020 (2. term)
Fall 2020 (3. term)
Spring 2021 (4. 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
PA195 NoSQL Databases
PA200 Cloud Computing
PA212 Advanced Search Techniques for Large Scale Data Analytics
PA220 Database systems for data analytics
PV062 File Structures
PV065 UNIX -- Programming and System Management I
PV079 Applied Cryptography
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

Recommended course of study

Fall 2019 (1. term)
Spring 2020 (2. term)
Fall 2020 (3. term)
Spring 2021 (4. 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 Advanced Techniques of Natural Language Processing
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 Introduction to 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
PB106 Corpus Linguistic Project I
PA107 Corpus Tools Project

Recommended course of study

Fall 2019 (1. term)
Spring 2020 (2. term)
Fall 2020 (3. term)
Spring 2021 (4. term)