Course title with a short description

Semester

Hrs

ECTS
credits

Deep Learning

Definition of deep neural networks as a specific paradigm of machine
learning, optimisation and modelling. Definition of model parameters and
hyperparameters. Discussion of the modular characteristics of deep models.
Description of the most important and most frequently used elements of deep
neural networks, including dense, convolutional, aggregation, folding,
reduction and residual layers. Non-linear and normalising components. Loss
function and characteristics of the most commonly used loss functions.
Learning through hetero- and auto-association. Implementation of deep neuralnetwork algorithms. Deep unsupervised learning models, in particular for
cluster analysis. Generative models (GAN). LSTM, GRU. Large Language Modles.

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summer

45

4

Modelling and Analysis of IT Systems

Business and object-oriented modelling methods of IT systems. UML modelling
of IT system requirements, statics and dynamics. Principles of choice UML
diagrams and recording of connections between theirs elements. Realization of
selected UML constructions in object-oriented programming languages.
This subject is aimed at extending knowledge and improve skills of students in
the field of software engineering, by familiarizing them with
- practical methods for analyzing, modeling, and designing an IT system so as to
optimize its architecture, implementation and deployment,
- selected methods for evaluating and optimizing systems in terms of
performance, reliability, and resource consumption,
- advanced methods for using the UML and SysML to describe and develop
systems.

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winter

30

3

Rule-based and Expert Systems

OPIS: Using rules and facts to representing knowledge, inferring, and making
decisions. The architecture of a system that uses a rule engine. Applications of
the rule-based approach. Expert systems and knowledge-based systems versus
business-rule systems and BRMS software. Technologies for developing rule-
based and expert systems. Methods for gathering knowledge and constructing
rules and facts. Problems with rule processing: conflict resolution strategies
and uncertainty modeling. Hybrid AI systems that use explicit representations
of knowledge.

This subject is aimed at familiarizing students with
- working principles of expert (knowledge-based) systems and rule-based
systems,
- application fields of expert/knowledge/rule-based systems,
and at developing student's skills in designing and implementing practical rule-
based systems by using selected technologies.

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summer

30

3

Virtual Technologies and Containers

The course aims to familiarize students with the concepts of virtualization
and containerization technologies. Installation and configuration of virtual
machines and containers.

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winter

30

3

Advanced Databases

Introduction to PL/SQL. Language rules. Data types. Blocks. Variables and
their scope. Conditional statements. Loops. SQL in PL/SQL. Records. Cursors.
Collections. Exceptions. Creating and using procedures, functions and packages.
Triggers. Dynamic SQL: NDS and DBMS_SQL. Introduction to objects in PL/SQL.

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winter

45

4

Advanced Object Programming

Familiarization with advanced object-oriented programming mechanisms.
Ability to use reflection in Java. Generic programming. Aspect-oriented
programming.

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winter

45

5

Internet Data Analysis

Types of data. Overview of qualitative analyses. Web data analysis.
Testing. Competitive analysis. User flow analysis. New forms of analysis: social
media, mobile services, and video content. Software supporting web data
analysis.
Course objectives and goals: To familiarize students with the basic concepts ofweb data analysis. To familiarize them with basic metrics, methods for
preparing for and conducting research, effective analysis mechanisms, and
modern tools.

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winter

30

3

Advanced Algorithms and Data Structures

Advanced graph algorithms: all-pairs shortest paths, flow networks.
String matching algorithms. Advanced data structures. Approximation
algorithms. Parallel algorithms.
Aims of teaching:
- To broaden student's knowledge of algorithms, data structures, and
algorithmic techniques.
- To improve student's skills in developing and studying algorithms.

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winter

45

4

Mathematical Analysis 1 (for Informatics)

Sets of numbers. Relations, elementary functions of a real variable and their properties. Mathematical induction. Sequences. Numerical series. Limit of a function of one variable. Asymptotes of a function. Continuity of a function. Derivative of a function of one variable and its properties. Derivative of an inverse and composite function. Increments and differentials. Extrema of a function of one variable. L'Hopital's rule. Higher-order derivatives.

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winter

75

6

Mathematical Analysis 2 (for Informatics)

Power series. Taylor series. The concept of an antiderivative and indefinite integral. Integration of rational, irrational, and trigonometric functions. Riemann's definite integral. Improper integral. Elements of topology, metric space. Functions of several variables: domain, limits, graphs. Partial derivatives. Schwarz's theorem. Directional derivative, gradient. Derivative of an implicit function. Extrema of functions of several variables. Jacobian. Polar coordinates. Double and triple integrals over a normal domain. Applications of integrals in geometry and physics.

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winter

75

6

Logic and Set Theory

Expressing thoughts formally and correctly, reasoning using logical tools. Fundamental notions and methods necessary to understand more advanced mathematical theories. Formally constructing and modelling mathematical objects on settheoretical grounds.

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winter

60

5

Differential and Difference Equations

Types of ordinary differential equations, methods of solving differential equations, certain applications of first-order differential equations, difference equations.

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winter

30

2

Medical Informatics (elective course)

Various methods of signal and image analysis, computer systems and software at different levels of health care, telemedicine and medicine on the Internet and the possibility of practical applications.

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winter

45

4

Bioinformatics (elective course)

Introduction to molecular biology. Bimolecular sequence analysis. Biological databases. Introduction to structural bioinformatics. Introduction to R and Python and their applications in bioinformatics. Biopython and Bioconductor libraries. Implementation of bioinformatics workflows in R and Python.

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winter

45

4

Functional Programming (elective course)

Introduction to lambda calculus, acquaintance with functional programming paradigm, acquaintance with selected functional language.

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winter

45

4

Elements of Automata and Formal Languages Theory

Introduction to the Theory of Automata and Formal Languages: Basic issues: the language and grammar, regular grammars, context-free grammars, context-sensitive grammars, finite 5 automata, pushdown automata, Turing machines, non-determinism, Chomsky hierarchy, characterization of the problems due to the undecidability and complexity.

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winter

60

4

Java Programming

Introduction to the Java language. Object oriented programming: inheritance, polymorphism. Exceptions – defining and using. Generic programming: parametrized types, collections, comparators, iterators, algorithms. Graphical user interfaces. Event programming.

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winter

75

6

Software Engineering 2

The rules of development of complex systems with particular consideration of implementation as phase. The four main programming paradigms (imperative, functional, object-oriented and logic) as a fundamental style of computer programming, as well the basic software design patterns will be discussed.

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winter

60

4

Databases

Introduction. Relational model. Relational algebra. SQL language. Designing relational databases. Normalization. Normal forms. Conceptual design. Entity relationship diagram. Logical design. Physical design. Basic file structures. Indexes. Transactions. Concurrency. Optimization.

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winter

60

5

Algorithms and Data Structures

Basic data structures (lists, stacks, queues, hash tables, trees, graphs), algorithms (graph algorithms, pattern matching in strings) and different methods of their design (“divide and conquer”, dynamic programming, greedy methods) and also estimation of their complexity.

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winter

60

4

Python Programming

Python Environment. Programming paradigms (structural, object-oriented,
functional) in the context of Python. Elements of network programming.
The designing and implementation of programs using selected packages and modules. The course includes elements of processing and analysis of big data sets.

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summer

45

4

Mathematical Analysis 2 (for Informatics)

Limit of one variable function. Actions on functions and their boundaries. Asymptote functions. Continuity of function. Derivative of the function of one variable and its properties. Derivative of the inverse and composite function. Differential of a function. Extrema of functions of one variable. L’Hospital’s rule. Derivatives of higher orders. Taylor series. Power series. Sequences and series functions. Antiderivative indefinite integral. Integration of rational, irrational and trigonometric functions. The Riemann integral. Improper integral.

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summer

60

5

Artificial Intelligence

Rough sets. Fuzzy sets. Artificial neural networks. Classification and clustering algorithms. Search methods. Evolutionary algorithms. Practical part: Application selected classification/clustering algorithms to data sets and reporting on the results.

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summer

60

4

Discrete Mathematics

Induction and recursion, the basics of combinatorics, basic techniques of counting, the basics of graph theory, the basics of number theory.

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summer

60

5

Probabilistic Methods and Statistics

Random variable, The probability of discrete and continuous, Probability distributions, Expected values, Variance, Standard deviation, Stochastic processes, Sampling, The problem of estimation, Testing statistical hypotheses, Correlation and regression, Computer methods of statistics.

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summer

75

6

Software Engineering 1

The typical software lifecycle phases, principles of systems design by the object method, software development tools, software requirements 4 specification, testing rules of software and software configuration management, project planning and software development process management.

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summer

30

2

Object-oriented Programming

Familiarize the students with the basic concepts and techniques of object-oriented programming on the example of C++. Practice the most important object-oriented techniques. Teach design, implementation and analysis of programs in the object-oriented paradigm.

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summer

75

6

Internet Programming

Basic Internet programming techniques, languages, tools and standards.

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summer

60

4

Network Technologies

Construction and operation of computer networks. OSI and TCP / IP – the protocols and features, IP addressing, routing, switching, Wide Area Networks (WAN), Virtual Private Networks (VPN).

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summer

60

4