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Questions UIZD

Common basis of the program

  1. Artificial intelligence methods: State space search, local search and metaheuristics with one solution and solution populations. Planning, problem representation, state space planning. Working with uncertainty, derivation in Bayesian networks, time and uncertainty. Robotics, robot motion planning. (IV126)
  2. Statistics: Thorough knowledge of basic statistical methods (point estimates, confidence intervals, testing of statistical hypotheses). ANOVA. Nonparametric hypothesis tests. Multiple linear regression, autocorrelation, multicollinearity. Principal Component Analysis (PCA). (MA012)
  3. Powerful computers and intensive computing: Superscalar, multicore and multicore (GPU, MIC) processors, MIMD and SIMD parallelism. Memory organization, shared and distributed, cache coherence. Code optimization, optimizing compilers. Distributed systems, network interconnection topology. Programming of parallel and distributed systems. (PA039)
  4. Database: Data storage, addressing of records. Indexing and hashing of multiple attributes, bitmap indexes, dynamic hashing. Query evaluation and algorithms, statistics and cost estimates. Optimization of queries and schemas, rules for query transformation, data distribution. Transaction processing, outages and recovery. Security, access rights. (PA152)
  5. Neural networks: Multilayer networks and their expressive abilities. Neural network learning: Gradient descent, back propagation, practical learning issues (data preparation, weight initialization, selection and adaptation of hyperparameters). Regularization. Convolutional networks. Recurrent networks (LSTM). (PV021)
  6. Machine learning: Thorough knowledge of basic machine learning methods (decision trees including regression, SVM, naive Bayes, logistic regression). Semi-supervised learning and active learning. Ensemble teaching. Basics of anomaly analysis. Advanced methods of experimental evaluation (cross-validation, ROC curves (AUC), M learning algorithms on N datasets, bootstrapping, Monte Carlo methods). Theoretical foundations of machine learning (generalization relations in propositional and predicate logic, space of hypotheses and versions, bias-variance trade off, VC dimension, PAC learning, inductive reasoning and Kolmogorov Complexity, complexity) (PV021, PV056)
  7. Knowledge acquisition: Data preprocessing. Learning frequent patterns and association rules. Tools for machine learning and data mining (in general + description of one in detail). Text mining. Description of one text mining method (categorization, disambiguation, information extraction) of your choice. (PV056, PV211. PA164)
  8. Visualization: Basic metrics for evaluating the quality of visualization (efficiency and expressiveness), eight basic visual variables. Basic visualization techniques for 1D, 2D, 3D (explicit and implicit surface representations) and 4D data (flow visualization using streamlines and Line Integral Convolution). Techniques for visualization of multidimensional data (parallel coordinates) and hierarchical structures (treemaps, dimensional stacking). Basic classes of interaction techniques (fisheye, perspective walls). (PV251, PV056)

Specialization - Processing and analysis of large data

  1. Data analysis. Search: principles, operators for data acquisition, evaluation of results, metrics. Similarity search. Distributed data processing, map-reduction technique and its applications, processing and filtering of data streams. Data warehouses and their life cycle, narrowed data warehouses (data marts), modeling (facts, dimensions) and implementation (star schema, data cube), extraction process, data transformation and uploading (ETL), data profiling, data integrity, data quality . (PA212, PA220, PA195)
  2. Applied cryptography. Symmetric and asymmetric cryptography, differences and uses. Hashing functions and their applications. Digital signature: construction, non-repudiation, management of public keys, certification authorities and public key infrastructure. Authentication, authorization and access control.
  3. Cloud computing and distributed databases. Cloud computing: basic principles, infrastructure as a service (IaaS), virtualization and containers, migration to the cloud, security of services, horizontal and vertical scalability. Current technologies (OpenStack) and cloud providers. Distributed databases: principles and advantages of NoSQL approach, consistency, data distribution. Key-value pair warehouses, document databases, graph databases, column family warehouses. (PA200, PA195)
  4. Software engineering. SW development process. Rational Unified Process methodology. Agile SW development. Testing phases and types of tests. Software metrics, code refactoring. Software quality. Estimation of costs and time of SW development. Maintenance and reusability. (PA017)
  5. Programming, organization and administration of files. UNIX system: kernel architecture, kernel memory model. Program: start and end, arguments, environment variables. Process: process attributes, process states, communication between processes (pipe, signals, reliable signals). Information theory, data coding and storage, data compression. Indexing and hashing: B + trees, linear and extensible hashing, locally sensitive hashing (LSH). File system: principles, data organization, external memory features, I / O operations, advanced I / O operations (multiplexing with select () and poll (), file locking, scatter-gather I / O, memory mapped I / O operations) , special files, distributed file systems. (PV065, PV062, PA152, PA212)

Specialization - Machine learning and artificial intelligence

  1. Probability in computer science: Discrete and continuous probability space. Random variable and its use. Mean, variance. Random processes, Markov chains. Information theory (entropy, mutual information), coding theory (Huffman coding, error channel capacity). (IV111)
  2. Computational logic: Complexity and computability of the satisfiability problem. Resolution method in propositional and predicate logic. Prolog language, relational algebra and Datalog. Tabular proofs in propositional, predicate and modal logic. Natural deduction. Inductive inferences in propositional and predicate logic. Bisimulation and temporal logic. (IA008)
  3. Natural language processing: Automatic morphological analysis. Recognition and generation of sentence structure, grammar, basic types of parsing. Semantic analysis of a sentence, logical analysis of natural language. Pragmatic level, communication situation. Speech processing, dialog systems. Corpora, statistical and rule marking. (PA153, IA161, PA156, PA154, IV029, PA164)
  4. Constraint programming: Consistency and algorithms for binary and non-binary conditions. Tree search, front view, back view, incomplete search. CPLEX OPL, modeling using constraint conditions, global conditions, constraint conditions for scheduling. (PA163)
  5. Machine learning. Logic and machine learning (multirelational learning). Metalearning and automated machine learning (AutoML). Advanced methods of anomaly analysis. Text categorization. Disambiguation by machine learning methods. Extraction of information from text. Keyword extraction. (PV056, PA164)

Specialization - Bioinformatics and systems biology

  1. Basics of bioinformatics. Fundamentals of molecular biology: structure of prokaryotic and eukaryotic cells, structure and function of nucleic acids and proteins, replication, transcription and translation. Bioinformatics, definition, field of interest, bioinformatics data. Genomics, genome and methods of its research, PCR, DNA sequencing, genome organization. Proteomics, proteome and methods of its research. Protein mass spectrometry. Basics of phylogenetics, methods of creating phylogenetic trees. Sequence similarity, sequence alignment, related algorithms. (IV107, IV108)
  2. Advanced methods of bioinformatics. Computational tools for genome analysis, in silico gene identification, genomic browsers. Biological sequences and information theory. DNA structure, RNA, heating temperature estimation and Nussin algorithm. Hidden Markov models and their use in bioinformatics. Advanced techniques for working with NGS data, metagenomics. Sequence search and genome annotation. Analysis of protein structures and their prediction from amino acid sequence. (IV108, PV269)
  3. Modeling and analysis of biological processes. Biological model specification: biological networks and pathways, static analysis of biological networks. Modeling and simulation of biological processes. Deterministic continuous model: law of active action of matter, kinetics of enzymes and gene regulation. Stochastic models: Markov chain of continuous time, stochastic Petri nets, Gillespi algorithm (SSA). Rule-based languages for the specification of biological models. Hypothesis specification using temporal logics, robustness of the model with respect to temporal properties. Qualitative models: Boolean networks and their analysis. (PB050, PA054)
  4. Continuous and hybrid systems. System definition, object, model, system. Dynamic system, transition function, system size, equations of state. Continuous, discrete, hybrid system. Examples of systems in biology. Linear and nonlinear systems, linearization. Stability and stability characterization. Attractors and domains of attraction, importance in biology. Parameterization, identifiability, estimation of parameters. The concept of reachability and manageability. Closed and open control circuits, examples of biological process control. (IV120)

Specialization - Natural language processing

  1. Natural language processing: Automatic morphological analysis. Recognition and generation of sentence structure, grammar, basic types of parsing. Semantic analysis of a sentence, logical analysis of natural language. Pragmatic level, communication situation. Speech processing, dialog systems. Corpora, statistical and rule marking. (PA153, IA161, PA156, PA154, IV029, PA164)
  2. Methods of knowledge representation and inference: Knowledge representation using rules, frameworks, semantic networks. Deductive and inductive derivation. Forward and backward chaining of rules. Derivation with uncertainty. Temporal logic, intentional logic and higher order logic. (PA153, IA008, IV029)
  3. Computational logic: Complexity and computability of the satisfiability problem. Resolution method in propositional and predicate logic. Prolog language, relational algebra and Datalog. Tabular proofs in propositional, predicate and modal logic. Natural deduction. Inductive inferences in propositional and predicate logic. Bisimulation and temporal logic. (IA008)
  4. Probability in computer science: Discrete and continuous probability space. Random variable and its use. Mean, variance. Random processes, Markov chains. Information theory (entropy, mutual information), coding theory (Huffman coding, error channel capacity). (IV111)