Courses taught in English for 2026-2027 Spring Semester

Annotation of Course

Antiderivative. Definite integrals and their application. Functions of several variables. Partial derivatives. Extreme values. First order differential equations. Higher order differential equations. Higher order linear differential equations with constant coefficients.

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

To introduce the methods of calculation of indefinite and definite integrals and their applications. Present classification of differential equations and their solutions.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96

Annotation of Course

The basic probability theory concepts and theorems. The distribution functions of random variables and numerical characteristics. The problems of mathematical statistics. Empirical characteristics. The point and interval estimates of unknown parameters. Statistical hypothesis testing, elements of correlation theory, regression.

Students must attend at least 60% of the time scheduled practical works and 50% of the lectures.

Aim of Course

To introduce basics of probability theory and mathematical statistics, to train a student to use obtained knowledge for solving of real world problems.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96

Annotation of Course

Integral calculus of functions of one variable: anti-derivatives, indefinite and definite integrals, the basic definitions, methods of integration, the properties of indefinite and definite integrals, Newton-Leibnitz formula, applications of definite integrals, multivariable functions, double and line integrals, ordinary differential equations and their solutions.

Students must attend at least 60% of the time scheduled practical works (full-time studies and part-time, distance learning studies) and 50% of the lectures (only full-time studies).

Aim of Course

The aim of this course is to provide the knowledge from integral calculus, to achieve the ability to analyze situation, to choose the appropriate problem solving method, to present and clarify the obtained results, to develop the ability use the knowledge and practical abilities in the study process.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96
Part-time, distance learning studies:
Lectures -26
Practical works -20
Laboratory works – 0
Consultations -4
Individual work -110

Annotation of Course

Combinations of elements in finite sets. Combinatorial numbers. Generating functions. Recursive equations. Relations theory. Ordered sets. Relations between rates of increase and decrease of number sequences elements. Graphs theory. Oriented graphs and relations. Loops in graphs. Algorithms of graphs analysis. Complicated tasks. Elemets of the information theory.

Students must attend at least 60% of the time scheduled practical works (full-time studies and part-time, distance learning studies) and 50% of the lectures (only full-time studies).

Aim of Course

Acquaint with the main concepts, problems and sollution methods of discrete mathematics. Students should know how to solve classical discrete mathematics problem after this course.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 0
Consultations -2
Individual work -48
Part-time studies:
Lectures -12
Practical works -4
Laboratory works – 0
Consultations -2
Individual work -62

Annotation of Course

The application of Artificial Intelligence (AI) methods in the field of information security is a game changer in many information security areas. This course is designed for students interested in exploring how AI methods such as machine learning and deep learning models can be leveraged to ensure information security. During this course, students will be introduced to key AI concepts, including machine learning, deep learning, and neural networks, as they apply to cybersecurity. Students will learn how AI-driven security solutions analyze vast amounts of data to identify anomalies, detect malware, and predict cyberattacks. AI use in intrusion detection systems, automated threat intelligence, and behavioral analysis of cyber threats will be also discussed. A significant part of this course will focus on practical applications and hands-on exercises, where students will apply ML and DL methods, security tools and frameworks for different type attack detection.

Aim of Course

To provide an understanding of the of modern artificial intelligence methods and their applications in the field of information security.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

The module is designed to provide students with advanced knowledge for Business Process Modeling and Simulation (BPMS) topics. The module covers the following BPMS topics: Introduction, Key Concepts, Context; Business Process Modeling (BPM), BPMN, Complex Business Process Modeling with BPMN, Business process choreography including BPMN solutions, Business Process and Business Rules, Business Process Simulation (BPS), BPSim.

Aim of Course

Provide students a basic and advanced knowledge on principles and methods of Business Process modeling and simulation.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

Module covers topics of business system analysis fundamentals; basic and advanced analysis techniques; system design fundamentals; design strategies and methods; system structure and architecture; design techniques and tools, user interface and experience design principles; design issues, design quality analysis and evaluation; enterprise architecture concepts, frameworks, and processes; legal, ethical, and social aspects of information system.

Aim of Course

To expand and deepen student knowledge of information system analysis and design process, activities and decisions, various methods and techniques, related standards and best practices, system design documentation, quality analysis and evaluation, enterprise architecture frameworks and processes. To strengthen student skills on applying various analysis techniques, proposing, and specifying information system design solutions.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 30
Consultations -0
Individual work -180

Annotation of Course

The module is designed to provide students with advanced knowledge for the informatics. The module covers the following topics:formal languages and formal grammar;Chomsky classification;formal first order languages;evaluation of terms and formulas;syntactic and semantics of languages; correctness and implementation of formulas in the module of a language;derivability of formulas and formal logical and domain theories, theorems and proofs;
concept of logical programming;finite automata theory; Touring automata;algorithm complexity;NP and effective algorithms to solve NP tasks;Kolmogorov complexity;Theory of a natural language.

Aim of Course

Provide students with a theoretical framework for computing a particular focus on the next topic: finite automaton, evidence of formal language and grammar theory, algorithms, complexity analysis.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

Malware analysis is one of the most critical skills for people, planning security analyst career. During this course, students will get known with malware, methods how malware is analyzed, what tools are used. Students that choose this course will create their own malware analysis laboratories where brief or in-depth malware analysis can be performed on infected documents, executables, media files. This course covers parts of reverse engineering, disassembly, network traffic analysis and other methods. Course is recommended for students, interested in incidence responses, work with malware and understanding how it acts and what methods exist to analyze and stop it.

Aim of Course

To get acquainted with malware functionality and how to detect it.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

The course provides knowledge of virtualization technologies, Linux containers, virtual infrastructure architecture, virtual repositories, clouds, and relevant security solutions used in these IT technologies. It teaches to evaluate security threats and vulnerabilities in virtual infrastructure and cloud computing and to make decisions that mitigate security risk in virtual infrastructure. Subject deals with security threats of hypervisor, virtual network, repositories, covers security aspects of virtual desktop infrastructure, virtual infrastructure maintenance and operational activities are analyzed. Security of cloud models and methods is reviewed, also data privacy, identity,contract management aspects in the clouds are anlyzed, cloud security standards are reviewed.

Aim of Course

Provide knowledge on virtualization technologies, virtual infrastructure and clouds, to develop ability to evaluate threats and vulnerabilities in virtuali infrastructure and clouds and to make decisions on security management in virtuali infrastructure and clouds.
Students must attend at least 60% of the time scheduled practical lectures.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

Ethical hacking and vulnerability scanning technologies are evaluated as compulsory part of any organization’s information system security evaluation. While studying this course students will get familiar with different vulnerability scanning technologies and learn how to act as an ethical hacker, detecting existing vulnerabilities in organization’s IT infrastructure without harming its functioning. The possessed understanding of methods and techniques used by hackers will also help the security specialist to plan appropriate countermeasures and evaluate their effectiveness.

Aim of Course

To provide students with knowledge and skills necessary to evaluate information systems security from outside of the organization.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 15
Consultations -0
Individual work -115

Annotation of Course

Electrostatics. Electrical properties of substances, theory of their conductivity. Magnetic field in vacuum and material. Maxwell’s equations for electromagnetic field. Wave optics. Light interaction with materials. Quantum mechanics. Laser. Biological effects of radioactive radiation.
Students are required to attend more than 50% of scheduled lectures and to complete all laboratory work.

Aim of Course

To give students the theoretical knowledge and problem-solving skills required in analysis and application of fundamental laws of nature.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 15
Consultations -2
Individual work -33
Part-time studies:
Lectures -14
Practical works -0
Laboratory works – 8
Consultations -2
Individual work -56
Part-time, distance learning studies:
Lectures -20
Practical works -0
Laboratory works – 10
Consultations -0
Individual work -50

Annotation of Course

This course introduces the fundamental physics of oscillatory and waves systems alongside their application in modern AI systems. Students will explore how ultrasound is used across various scientific and industrial fields. Through lectures, practical work and hands-on laboratory works, student will connect core physical principles to the interdisciplinary challenges.
Students are required to attend more than 50% of scheduled lectures and at least 60% of scheduled practical work, as well as to complete all laboratory work.

Aim of Course

This course aims to equip students with a solid understanding of oscillatory motion and acoustic waves, while bridging these physical principles to modern applications in artificial intelligence.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96

Annotation of Course

Applied Physics module gives knowledge on electrostatic field effects, explains appearance and dynamics of electric conductivity of materials and electric current that generates a magnetic field. Electromagnetic induction in vehicles. It explains physical properties of Electromagnetic waves that propagate through various media, laws of optics and it engineering applications, solid body concept, modern concept of atomic nuclear and connection with transport engineering.
Students are required to attend more than 50% of scheduled lectures and at least 60% of scheduled practical work, as well as to complete all laboratory work.

Aim of Course

To give students the theoretical knowledge of modern physics and practical skills required in analysis and application of fundamental laws of nature. To describe the academic achievements of emerging technologies in the content, to teach how to evaluate the theoretical and experimental data and their reliability.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 15
Consultations -2
Individual work -33
Part-time studies:
Lectures -6
Practical works -6
Laboratory works – 0
Consultations -2
Individual work -66

Annotation of Course

Materials in electric and magnetic fields. Electromagnetic oscillations and waves, their characteristics and laws. Photometry. Waves and quantum phenomena determined by electromagnetic radiation. Electrographic and neutronographic methods of the researches of materials and their application. Nuclear energy.
Students are required to attend more than 50% of scheduled lectures and at least 60% of scheduled practical work, as well as to complete all laboratory work.

Aim of Course

To give knowledge about materials in electrical and magnetic fields, electromagnetic waves, the spectrum of their appliance and the use in technique and technologies. To explain the nature of electromagnetic wave, to give modern view about the atom and nuclear physics, to characterize the content of created technologies achieved by physics science, to teach to estimate theoretical and experimental got data and their reliability, to prepare for the bioengineering studies.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 15
Consultations -2
Individual work -33

Annotation of Course

This study subject gives knowledge on electrostatic field effects, explains appearance and dynamics of electric conductivity, voltage, potential and electromotive force. Explained how electric current generates a magnetic field. It explains physical properties of electromagnetic waves, main laws of optics and it applications, the structure of atoms, crystalline structure of solids. It discusses a nature of atom’s nucleus and gives most modern ideas regarding composition of nucleons and describes a state of electrons in a crystal by means of theory of energy bands and principles of semiconductor devices operation.
Students are required to attend more than 50% of scheduled lectures and at least 60% of scheduled practical work.

Aim of Course

To give knowledge on subject of the behavior of charge in electric and magnetic fields, electromagnetic waves, their energy spectrum and application. To explain the quantum nature of electromagnetic waves, to describe the academic achievements of emerging technologies in the content, to develop scientific thinking skills, To give knowledge on subject of the behavior of materials in electric and magnetic fields, electromagnetic waves, their energy spectrum and application.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 0
Consultations -2
Individual work -48
Part-time, distance learning studies:
Lectures -8
Practical works -8
Laboratory works – 0
Consultations -2
Individual work -62

Annotation of Course

In contrast to the classical statistics, data is considered as fixed and it’s distribution parameters are considered as random in Bayesian statistics. Inference about the parameters and other unknown values is updated applying information obtained from the data. Using Bayesian theorem statistical models are constructed. Prior distributions, data likelihood, posterior distributions and their approximation are studied for this. Bayesian methods are applied for the moedls of the classical statistics. Advantages and disadvantages of classical and Bayesian methods are compared; acquitance with the estimation of the hierarchical models is made. Suitability of the statistical models is tested applying information criteria. R software is used for practical application of Bayesian methods.

Aim of Course

To make acquaintance with the principles of the Bayesian inference in statistics and apply this knowledge in practice.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

This course is designed to deepen students’ knowledge of advanced machine learning methods not covered in undergraduate studies. It focuses on more complex supervised and unsupervised learning algorithms, including regularization techniques, ensemble models (bagging, boosting), parametric and non-parametric classifiers, dimensionality reduction methods, and outlier detection techniques. The course also introduces the principles of Automated Machine Learning (AutoML) and emphasizes the importance of model interpretability in modern data science. Special attention is given to applying these methods to real-world data and interpreting the results to support data-driven decision-making.

Aim of Course

To provide students with deep theoretical and practical knowledge of advanced machine learning methods, develop their ability to apply complex algorithms in real-world data analysis, independently evaluate model suitability and interpretability, and prepare them for further work in the fields of data science and artificial intelligence.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

The course is devoted to the basics of the probability theory and mathematical statistics. The definitions for random event and probability are formulated, their main properties are presented. Random variables and their properties are studied, acquaintance with some discrete and continuous random distributions is made further. The second part of the course consists of the main principles of mathematical statistics. Methods for population parameter estimation and statistical hypothesis testing are overviewed. Each topic is illustrated by applications and solutions of practical tasks. Computer software application in statistics is introduced for students.
Students must attend at least 51% of the lectures, at least 80% of the exercises and at least 80% of the laboratory work during the scheduled time.

Aim of Course

To provide students with an understanding of the probability theory, to develop their ability to solve problems of mathematical statistics and apply their knowledge for statistical data analysis.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 15
Consultations -6
Individual work -159
Full-time weekend learning studies:
Lectures -20
Practical works -20
Laboratory works – 10
Consultations -6
Individual work -184
Part-time studies:
Lectures -18
Practical works -10
Laboratory works – 8
Consultations -6
Individual work -198

Annotation of Course

The main conceptions in creating linear regression and time series statistical models are presented in this course. The questions of a model identification, fitting the models parameters, model diagnostic and forecasting are considered. Besides theoretical knowledge students have to solve tasks on the black board and using the special statistical program package for full statistical analysis.
Students must attend at least 60 % of the time scheduled laboratory work.

Aim of Course

The aim of this course is to introduce the main econometrics models and to consider their application in practice.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -0
Practical works -0
Laboratory works – 30
Consultations -0
Individual work -50
Part-time studies:
Lectures -0
Practical works -0
Laboratory works – 10
Consultations -0
Individual work -70

Annotation of Course

Computational methods play significant role in modern statistical data analysis. This course is an introduction to the modern, computationally intensive methods in statistics. Topics include numerical optimization, least squares and maximum likelihoo methods, dimensionality reduction, simulation and Monte Carlo methods including Markov chain Monte Carlo (MCMC) and other modern topics.

Aim of Course

In this course, goal is to give students a foundation in common computational techniques and algorithms that are used in the implementation of statistical methods.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

In the course variuos methods of integration and aplication of the integral calculus in geometry, mechanics, etc., are discussed. Multiple and line integral are evaluated using polar, cilindrical, spherical coordinates, their aplication are discussed. Differential equations of the first and higher orders as well as system of differential equations are solved. Infinite series are aplied for variuos approximations.
Students must attend at least 51% of the lectures, at least 80% of the exercises and at least 80% of the laboratory work during the scheduled time.

Aim of Course

The aim of the course is to introduce notions of indefinite, definite, improper,,multiple integrals, to discuss aplications of the integral, to present elements of differential equations and infinite series.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96
Part-time studies:
Lectures -10
Practical works -6
Laboratory works – 6
Consultations -4
Individual work -134

Annotation of Course

The course is designed to introduce students to relational and distributed databases, their different types. Examples are given of how to create, insert, edit or retrieve data in each case. The advantages and disadvantages of each of them are presented.

Aim of Course

It will be given knowledge about relational and distributed databases, be able to create the database to meet their needs, be able to record, update and retrieve data from any type of database.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -45
Laboratory works – 0
Consultations -0
Individual work -180

Annotation of Course

The main conceptions in creating linear regression and time series statistical models are presented in this course. The questions of a model identification, fitting the models parameters, model diagnostic and forecasting are considered. Besides theoretical knowledge students have to solve tasks on the black board and using the special statistical program package for full statistical analysis.
Students must attend at least 51% of the lectures, at least 60% of the exercises.

Aim of Course

The aim of this course is to introduce the main econometrics models and to consider their application in practice.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -45
Practical works -15
Laboratory works – 0
Consultations -4
Individual work -96
Part-time studies:
Lectures -12
Practical works -0
Laboratory works – 12
Consultations -4
Individual work -132
Part-time, distance learning studies:
Lectures -12
Practical works -0
Laboratory works – 12
Consultations -4
Individual work -132

Annotation of Course

The module examines the main elements of FinTech programming. Introduces with Python programming language syntax, the main Python modules for FinTech.

Aim of Course

To introduce the main peculiarities of programming Python and the possibilities of using this programming language for FinTech.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -20
Practical works -0
Laboratory works – 20
Consultations -15
Individual work -185
Part-time, distance learning studies:
Lectures -6
Practical works -0
Laboratory works – 4
Consultations -8
Individual work -222

Annotation of Course

The course is devoted to the basics of the probability theory and mathematical statistics. The definitions for random event and probability are formulated, their main properties are presented. Random variables and their properties are studied, acquaintance with some discrete and continuous random distributions is made further. The second part of the course consists of the main principles of mathematical statistics. Methods for population parameter estimation and statistical hypothesis testing are overviewed. Each topic is illustrated by applications and solutions of practical tasks. Computer software application in statistics is introduced for students.
Students must attend at least 51% of the lectures, at least 60% of the exercises and at least 60% of the laboratory work during the scheduled time.

Aim of Course

To provide students with an understanding of the probability theory, to develop their ability to solve problems of mathematical statistics and apply their knowledge for statistical data analysis.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 15
Consultations -6
Individual work -159
Full-time weekend learning studies:
Lectures -20
Practical works -20
Laboratory works – 10
Consultations -6
Individual work -184
Part-time studies:
Lectures -18
Practical works -10
Laboratory works – 8
Consultations -6
Individual work -198

Annotation of Course

The module explores the fundamental laws of chemical thermodynamics, elements of statistical thermodynamics, as well as equilibrium conditions and their significance in biological and engineering systems. It addresses the thermodynamic principles governing chemical, biochemical, and biophysical processes, phase equilibria, surface phenomena, adsorption, dispersed systems, and the thermodynamic aspects of electron and ion transport in living systems.
Students must attend at least 80% of the time scheduled practical lectures and at least 50 % of theoretical lectures.
Students must attend 100% of the time scheduled laboratory work.

Aim of Course

The aim is to provide students with an in-depth understanding of the fundamental principles of chemical thermodynamics and kinetics to develop their ability to apply this knowledge in the analysis and modelling of biochemical, biophysical, and bioengineering processes.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 30
Consultations -0
Individual work -85

Annotation of Course

Pollutants. Risk assessment. Microorganisms and biodegradation. Technologies of pollutants decontamination. Bioreactors. Treatment of polluted water bodies. Treatment of wastewater. Treatment of polluted soil. Decontamination of solid waste, composting. Plants for pollutant treatment.
Students must attend at least 70 % of the scheduled practical sessions and at least 50% theoretical lestures. Students are required to complete all scheduled laboratory work.

Aim of Course

Awareness of the environmental biotechnology purposes, concepts and principles and the use of contaminated environmental cleaning

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 15
Consultations -2
Individual work -33

Annotation of Course

It is a set of theoretical knowledge and practical skills that help understand the formation and properties of non-elektrolyte and elektrolyte solutions, oxidation-reduction processes, which are taking place in electrochemical devices, electrolysis, and corrosion of metals, which are very important in rapidly changing in operational situations in the field of elektronics engineering. Understanding of the studied chemical processes or phenomena and the ability to manage them will contribute to effective work in this field of engineering.
Students must attend at least 70 % of the scheduled laboratory work and at least 50 % of theoretical lectures. Students are required to complete all scheduled laboratory work.

Aim of Course

To provide the students with chemistry knowledge necessary in the studies of the general and special courses as well as in the practice activities.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -0
Laboratory works – 15
Consultations -2
Individual work -48
Part-time, distance learning studies:
Lectures -10
Practical works -0
Laboratory works – 10
Consultations -2
Individual work -58

Annotation of Course

Exploring life on its different levels. The chemistry of life and the structure of procariotic and eucariotic cell. The eucarytoic cell cycle, mitosis and meiosis. The role of meiosis in an organism life cycle. G. Mendel’s discoveries. Mendelian inheritance in humans. Basics of human genetics. DNA as the genetic material, DNA replication. The chemical structure and function of carbohydrates, lipids and proteins. Concept of transcription and translation. The organization of eukaryotic genomes. DNA cloning and practical application. An introduction to the viruses, viroids and bacteriophage, their genomes and reprodutcion. The evoliution of populations, the factors affecting the evolution.
Students must attend at least 70 % of the scheduled practical sessions and at least 50 % of theoretical lectures.

Aim of Course

To understand the basics of general biology.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96

Annotation of Course

It is a set of theoretical knowledge and practical skills that help to understand the types of material analysis tasks, samples preparation, analysis method selection, which are very important in the field of biotechnology. The accuracy is very important for the analysis results.
Students must attend 100% of the time scheduled laboratory work and at least attend 50 % of theoretical lectures.

Aim of Course

To enable students to pursue chemical knowledge by applying various analytical methods, devices and strategies to obtain information about the composition and structure of substances.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 30
Consultations -4
Individual work -96

Annotation of Course

Historical and philosophical review of modeling; modeling software (Maple use in modeling). Recurrence and differential equations of growth. Numerical methods for problem solving. Interpolation and regression. Interaction of populations. Enzyme kinetics (Henri-Michaelis-Menten model, King-Altman method, kinds of inhibition). Nonlinear chemical kinetics. Statistical models in biology. Course project – review of articles or modeling of biological phenomena.
Students must attend at least 70% of the time scheduled practical work and at least 50% theoretical lestures.

Aim of Course

Present the methods of modeling of biological systems, to teach modeling with the standard Wolfram Mathematica and Excel programs.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96

Annotation of Course

In this course, students learn about modern liquid chromatography techniques and their classifications. Chromatographic methods are analyzed based on their separation mechanisms and potential applications.
Students must attend at least 80% of the time scheduled practical lectures.
Students must attend 100% of the time scheduled laboratory work.
Student have to attend not less then 70 % theory lectures.

Aim of Course

The objective is to provide students with the fundamental principles of chromatography, including liquid chromatography methods and their applications.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 30
Consultations -4
Individual work -81

Annotation of Course

The course mainly focuses on biochemical recognition process and its implementation in different analytical methods. Spectral and electrochemical methods and their application in analysis of biomolecules, as well as enzymatic analysis methods, methods of biomolecules immobilization and methods of immunoanalysis are analyzed in detail. A lot of attention is paid for application of statistical methods in laboratory.
Students must attend at least 70 % of the scheduled practical sessions. Students are required to complete all scheduled laboratory work. Student have to attend not less then 70 % theory lectures.

Aim of Course

Introduction to the biochemical analysis methods in biochemistry, medicine, food quality control and environmental protection.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 30
Consultations -4
Individual work -81

Annotation of Course

The fundamentals of gene engineering investigate structure of prokaryotic and eukaryotic genes, regulation of gene expression; characteristics and structure of vectors; enzymes involved in gene engineering process; construction of recombinant DNA and introduction into tested organism as well as methods of gene detection; theoretical and practical basis of isolation, purification, qualitative and quantitative analysis of recDNA.
Students must attend at least 70 % of the scheduled practical sessions. Students are required to complete all scheduled laboratory work. Student have to attend not less then 70 % theory lectures.

Aim of Course

The main purpose of the course is to assimilate the concept of gene engineering and understand the practical possibilities of implementation of gene engineering.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 30
Consultations -4
Individual work -81

Annotation of Course

Bioinformatics is becoming essential to the work of every biologist. The course introduces the aims and objectives of bioinformatics, familiarises students with structures and functions of DNA and proteins, and demonstrates different ways of finding, comparing, and predicting different biological sequences and structures.
Students are required to complete all the laboratory activities scheduled.
Student have to attend not less then 70 % theory lectures.

Aim of Course

To enable students to practice in silico bioanalysis using open biological databases, and familiarize students with fundamental bioinformatics algorithms and their application.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -0
Laboratory works – 30
Consultations -4
Individual work -111

Annotation of Course

Chemistry and bioorganic chemistry of amino acids, peptides and proteins. Carbohydrates, monosaccharides, polysaccharides, glycosides. Components of nucleic acids, DNA and RNA. Lipids, steroids, terpenes. Naturally occurring biologically active compounds. Structure determination methods of bioorganic molecules.
Students must attend at least 70 % of the scheduled practical sessions and at least 50 % of theoretical lectures. Students are required to complete all scheduled laboratory work.

Aim of Course

Aware of the biologically important organic substances (proteins, carbohydrates, lipids, DNA and RNA, bioreguliators).

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 30
Consultations -6
Individual work -144

Annotation of Course

The biology and technology course of stem cell has to deal with ethical questions which are related to stem cell research and use in development of technologies. It examines the types of stem cell, their characteristics and their secretion sources. The course is provided the mechanism of stem cell cell-renewal ability and a stem cell cycle regulation during the development. The course brings a fundamental knowledge of stem cell differentiation. During the course you can get an interpretation about the multipotence basis of the stem cell in adult organisms and to find their recesses in the human body. In practice classes students will be able to observe and to document the changes in the morphology of individual cells during differentiation. Students are introduced to nanotechnologies in stem cell biology and technology. The course gives a comprehensive explanation about the cell transplantation technologies in case of different diseases and about the developed cell-based Nano biotechno

Aim of Course

To familiarize students with the types and characteristics of stem cells, which make them different from other cells. To present them new research-based materials on the development of stem cells biology and how it could be used in tissue engineering and by creating new medical technologies. To familiarize students with nanotechnology in stem cell biology and technology. To introduce students to the fundamental features of cell technology including disease treatment and legal aspects.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 15
Consultations -0
Individual work -115

Annotation of Course

Science and its classification. Fundamental and applied research. Organization and planning of research process. Collection of experimental data and their confidence. Dissemination of research results. Electronic resources of scientific publications. Bioethics issues in biotechnology. Patents and their registration. Patent resources. Patents in biotechnology. Innovations, the importance and types. Innovations in biotechnology.

Aim of Course

To acquaint with the organization and planning of research process ant with the dissemination of research results.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

The application of the immune system elements in the development of new immune technolgies and bioengineering. Manipulation with immune system cells and generation of advanced therapy medicinal products. Immunotechnology and modern methods of immunotherapy. Practical aspects of antibody engineering and manufacturing. Use of immunobiotechnology in diagnostics, medicine and industry.

Aim of Course

To provide with knowledge, necessary for undestarding of immunotechnological processes and their control.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -0
Individual work -100

Annotation of Course

Nanomedicine, the application of nanotechnology in health care, offers numerous very promising possibilities to significantly improve medical diagnosis and therapy. It exploits the improved and often novel physical, chemical and biological properties of materials at the nanometric scale. Nanomedicine has the potential to enable early detection and prevention, and to essentially improve diagnosis, treatment and follow – up diseases. The three areas are addressed in the present course: Nano-diagnostics, Nano-pharmaceutics and Regenerative Medicine.

Aim of Course

Providing the interdisciplinary knowledge on the newest scientific achievements and developing methods in the field of nanotechnology, its applications in the studies on biological objects and for the needs of health care.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 15
Consultations -0
Individual work -115

Annotation of Course

Practical and theoretical knowledge about software testing is presented in this module. Testing principals, levels, taxonomy are introduced. Testing methods and tools for funcional and nonfuncional testing are described. The tools and methods are used practically during lab works. Web application testing, web services testing are introduced. Automation of testing is described. Automated tests’ generation and execution frameworks and tools are presented and used by the students during therethical course and labs.
Students must attend at least 80% of the time scheduled practical work and at least half of the lectures at the scheduled times.

Aim of Course

Obtain basic software testing theoretical and practical knownledge.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -24
Laboratory works – 0
Consultations -4
Individual work -108

Annotation of Course

To provide basic knowledge of object-oriented design and programming in the C++ language. Considering object-oriented design rules, form the foundations of rational software design. The knowledge gained during this course becomes an introduction to studying other programming languages. After mastering the course, the student must be able to design and create object-oriented software and use tools to develop this equipment.
Students must attend at least 80% of the time scheduled laboratory work and at least half of the lectures at the scheduled times.

Aim of Course

To provide basic knowledge of object-oriented design and programming in C++ programming.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 30
Consultations -4
Individual work -96

Annotation of Course

Object-oriented programming course based on the Java programming language. The course will cover principles of object oriented programming: classes, objects, encapsulation, inheritance, polymorphism, visibility of class elements. Memory management, exception management, standard Java libraries, networked features of Java technology are introduced. The course provides examples on good programming practice and Java language usage for development of mobile applications (Android) and web systems (JEE).
Students must attend at least 60 % of the time scheduled practical lectures and at least 80 % laboratory works and at least half of the lectures at the scheduled times.

Aim of Course

To provide object oriented programming knowledge and skills to apply it in Java programming language.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -12
Laboratory works – 24
Consultations -4
Individual work -96
Part-time studies:
Lectures -12
Practical works -0
Laboratory works – 10
Consultations -4
Individual work -134
Part-time, distance learning studies:
Lectures -16
Practical works -0
Laboratory works – 10
Consultations -5
Individual work -129

Annotation of Course

The course is designed to acquaint students with the concepts of relational databases and relational database management systems, the principles of their work. Provides information on possible actions with database data using commands and operators. Normalization of databases to the third normal form is introduced.
Students must attend at least 80% of the time scheduled laboratory work and at least half of the lectures at the scheduled times.

Aim of Course

To get acquainted with the main principles of database work using SQL.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -0
Laboratory works – 24
Consultations -4
Individual work -108

Annotation of Course

Students will be introduced to principles of distributed systems, will analyze how can be distributed data within different systems or services, how it is processed and used. Subject is oriented to programming, application solutiosn ratgher than technical details. Therefore topics of MySQL, Oracle databases are included to address existing solutions for data distribution, distributed file systems, P2P, RMI and other similar techynologies are analyzed.
Students must attend at least 80% of the time scheduled laboratory work.

Aim of Course

To teach students properly understanding of project management concept, as a view to modern method of management, to give theoretical and practical knowledge about types of projects, their purposes, cycle of project implementation, structure of business (activities) plan, to provide knowledge on the principles of project team formation, peculiarities of supervisor selection, business project control and changes performance management, project risk evaluation and management methodology, project m

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -12
Laboratory works – 12
Consultations -4
Individual work -108
Part-time, distance learning studies:
Lectures -12
Practical works -6
Laboratory works – 6
Consultations -4
Individual work -132

Annotation of Course

This course introduces the basic concepts and terminology of relational databases, as well as to the concepts of data modelling and the creation of entity relationship diagrams (ERD) and the corresponding representation of the ERD, the features of DB design. Provides information about possible actions with database data using SQL commands and operators. The course also introduces the design features of relational databases. During the course, students will create projects designed to demonstrate a database solution for a business or organization.
Students must attend at least 80% of the time scheduled laboratory work.

Aim of Course

To provide information about the stages of database development with the help of entity relationship diagrams, to learn how to design a database, use SQL language and create data entry forms for database end users.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -0
Laboratory works – 24
Consultations -4
Individual work -108
Part-time, distance learning studies:
Lectures -14
Practical works -0
Laboratory works – 8
Consultations -2
Individual work -136

Annotation of Course

In this course, students develop skills in applying object-oriented programming principles to practical Python programming focused on data processing and analysis. Students learn to create classes and manage their objects, apply inheritance and polymorphism, and effectively use exception mechanisms and modules to create structured, modular code. The course focuses on solving real-world problems using popular Python libraries: NumPy for operations with single- and multidimensional arrays, Pandas for structured data management, import/export, and processing, and matplotlib for visualization of results. All work is done using only Python syntax and modern, open libraries.

Students must attend at least 60 % of the time scheduled practical lectures and at least half of the lectures at the scheduled times.

Aim of Course

To provide students with deep practical skills in the object-oriented programming paradigm applied in the Python language, based on modern data processing and analysis libraries.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96
Part-time studies:
Lectures -8
Practical works -8
Laboratory works – 0
Consultations -4
Individual work -140

Annotation of Course

Digital image fundamentals. Human visual perception. Image enhancement in the spatial domain. Image enhancement in the
frequency domain. Image noise in image processing. Color image processing. Image restoration. Image segmentation.
Morphological image processing. Binary image feature extraction and analysis.
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.
Students must attend at least 80% of the time scheduled laboratory work.

Aim of Course

To introduce the main methods of digital image processing, with main algorithms and their practical realization. To gain skills to work with the commercial image processing program and to learn its advanced possibilities.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96
Part-time studies:
Lectures -8
Practical works -4
Laboratory works – 4
Consultations -4
Individual work -140

Annotation of Course

Visualization pipeline. Data representation and formats. Basic algorithms of information visualization. Visualization software. Interaction of visualization systems. Types of video mapping. Technologies of video mapping.
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.

Aim of Course

Basic knowledge of developing visualisation systems and video mappings.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -30
Laboratory works – 0
Consultations -2
Individual work -33
Part-time studies:
Lectures -4
Practical works -8
Laboratory works – 0
Consultations -2
Individual work -66

Annotation of Course

During theoretical lectures students are introduced to acoustic and psychoacoustic, digital audio signals, their characteristics, most widely used standards and formats. Deeper understanding is provided about digital audio signals processing methods, various kinds of noises and their cancelation methods, audio editing technologies. In laboratory, the students code audio processing systems with MATLAB, edit audio files with Adobe Audition. There is coursework which consists of a theoretical analysis and practical part, during which are created sound processing systems or produced audio records.
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.

Aim of Course

After completion of this course students will be aware of:
1. basic parameters, properties, standards and formats of sound;
2. audio files editing methods, techniques and technologies;
3. digital audio signal processing principles and algorithms;
4. will be able to create various sound signal processing systems.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96
Part-time studies:
Lectures -8
Practical works -8
Laboratory works – 0
Consultations -4
Individual work -140

Annotation of Course

Applications of computer graphics. Graphics hardware. Graphics primitives and their implementation in software. Transformation engine. Fixed function graphics pipeline. Rendering algorithms. Visibility algorithms: Z buffers. Culling. Light sources. Illumination. Shading algorithms (Flat, Gouraud, Phong). Programming of special-purpose computing devices. GPU programming. Programming of interaction between graphical application and user.
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.

Aim of Course

Basic knowledge of computer graphics algorithms and programming technologies of computer graphics. After this course students will be able:
to develop application of computer graphics;
to render 3D objects;
to implement user interaction.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96
Part-time studies:
Lectures -8
Practical works -8
Laboratory works – 0
Consultations -4
Individual work -140

Annotation of Course

Website design involves planning and managing of the website content represtented as visual, textual and information. The module examines website types, adaptive and adaptive design; website information architecture, navigation design, web page style definition, inportance of CRAP principles, website animations, content optimization for search engines (SEO).
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.
Students must attend at least 80% of the time scheduled laboratory work.

Aim of Course

Students will learn and be able to:

1. use HTML5 to define the structure and content of websites, CSS3 – to describe the design of websites.
2. choose appropriate technologies for implementing interactive elements of web pages or micro-animation.
3. understand the importance of consistency, structure and aesthetics in website design
4. understand modern website design trends (including layout of individual pages, colors, fonts, interactivity, animation, technological implementation)
5. optimize the website content (textual and visual) for the search engines (SEO optimisation)
6. understand the current W3C standards and recommendations, be able to apply them in the web page development process
7. program web page design using front-end frameworks or specialized libraries

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 15
Consultations -2
Individual work -33
Part-time studies:
Lectures -4
Practical works -4
Laboratory works – 4
Consultations -2
Individual work -66

Annotation of Course

Students are acquainted with human-computer interaction (HCI) theory, psychological factors of HCI, general HCI design principles, guidelines and templates. They are also introduced to user interface types and their design peculiarities. During the practices, students perform independent tasks, which include the theoretical (analysis) and practical parts,usability testing of the digital products. The development of user interfaces for mobile apps based on user-centred design principles is analysed during the course. FIGMA software is used for the design of the interactive prototype of a mobile app user interface.
Students must attend at least 70% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.

Aim of Course

After this course students should be able to:
apply basic knowledge of HCI principles and techniques;
– be able to design user interfaces of the digital products applying the user-centred design principles;
– be able to analyze user needs,
– be able to analyze the usability of the digital products
– work effectively in teams developing digital products

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96
Part-time studies:
Lectures -8
Practical works -8
Laboratory works – 0
Consultations -4
Individual work -140

Annotation of Course

Video technologies course combines the most important aspects of modern video creation, processing and evaluation in post-production – creation of graphic elements, video effects and motion graphics, their compositing, integration and management in video content or their composition. During the course students are taught to create video supplemented with fictitious elements or video effects, where creation of graphic content, motion graphics and their management processes are automated by expressions, expressions controls and scripts using Adobe After Effects.
Students must attend at least 70% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.
Students must attend at least 70% of the time scheduled laboratory work.

Aim of Course

Having finished the course the students will have knowledge of:
1. Selection and evaluation of suitable technologies and methods for video processing;
2. Structuring, importing, and usage of external vector data for video content creation;
3. Creation of motion graphics and its management in video content using expressions and expressions controls;
4. Writing scripts of creation and management of video project and its elements.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96
Part-time studies:
Lectures -8
Practical works -4
Laboratory works – 4
Consultations -4
Individual work -140

Annotation of Course

During the course, students will be introduced to the classification and types of 3D models, their modeling methods, the creation of virtual materials and UV maps, and visualization of 3D models. Upon completion of the learning process, students will be able to model precise solid objects and organic surface objects by using AutoCAD and Blender software. Students will also be able to texture and visualize the created models. Will be able to choose the right type of model to solve a specific modeling task, prepare the created models for work in other applications.
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.
Students must attend at least 80% of the time scheduled laboratory work.

Aim of Course

After completing the learning process, the students will be able to use create organic and precise 3D models. Students will understand the structure of 3D objects and will be able to apply universal and typical geometrical modeling methods, including polygonal and spline curve-based methods, to create them. Students will also be able to texture and visualize created 3D objects and prepare them for work in other applications.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 30
Consultations -4
Individual work -96
Part-time studies:
Lectures -4
Practical works -4
Laboratory works – 8
Consultations -4
Individual work -140

Annotation of Course

Both traditional and modern artificial intelligence (AI) techniques that are applied in computer games are studied. At the beginning, general concepts of AI are discussed such as common models, algorithms, data structures. Later the focus of the lectures shifted to the aspects of AI in computer games: character movement, pathfinding, decision-making, strategy, tactics, learning, and creativity.
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.

Aim of Course

Know and be able to use artificial intelligence methods for modeling the behavior of characters.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -96
Part-time studies:
Lectures -8
Practical works -8
Laboratory works – 0
Consultations -4
Individual work -140

Annotation of Course

This course introduces with multimedia design elements, promotional products design features, their aesthetic, expression and the importance of conveying and expressiveness of information. The presentation of multimedia design elements for the specifics of using graphical means of expression and compositional principles. Analyzing visual design and development cycle of the imaged object. Taught create abstracted image of the object, to create the specific environment.
Introduced in style design, taught to create a representative multi-media product design. Taught to choose and combine font style, proportions, composing.
Students must attend at least 80% of the time scheduled practical lectures and at least half of the lectures at the scheduled times.
Students must attend at least 80% of the time scheduled laboratory work.

Aim of Course

Be able to apply the principles of multimedia design, to select right elements of artistic expression, to create aesthetically, expressive and evocative computer visualization.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -30
Laboratory works – 15
Consultations -4
Individual work -96
Part-time studies:
Lectures -4
Practical works -8
Laboratory works – 4
Consultations -4
Individual work -140

Annotation of Course

The applicability of Gis is constantly expanding and many engineering students need to know the basics in order to use it in their field of work. During this course, knowledge about the principles of GIS operation and analysis capabilities will be provided. The applicability of using GIS in different fields is also reviewed.

Aim of Course

To introduce the theory of GIS analysis and practically apply GIS software for data analysis.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -10
Practical works -0
Laboratory works – 20
Consultations -0
Individual work -50

Annotation of Course

Drawing devices, tools and accessories. Formation of drawings: standards, scales, lines, fonts. Projection methods, projections of points, lines and planes. Projection of parts into three projection planes; viewing of international holes, cavity; views and their displacement in drawings; sections, incomplete sectional views, enlarged part view elements, corditionalities and simplifications. Solid modeling according to pictorial projections and to the views given. Surface modeling. Surfaces intersection and their developments.
Students are required to attend at least 80% of the practical and laboratory classes at the scheduled time during the semester. Mandatory minimum attendance of module lectures – 50%.

Aim of Course

To present graphical problems solving algorithms, to develop abilities to read and make engineering drawings, adapting theoretical knowledge and according to drawing standards requirements and also to manage computer – aided design tools, to develop spatial imagination.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -30
Laboratory works – 15
Consultations -4
Individual work -96
Part-time studies:
Lectures -6
Practical works -10
Laboratory works – 10
Consultations -4
Individual work -130
Part-time, distance learning studies:
Lectures -20
Practical works -10
Laboratory works – 10
Consultations -0
Individual work -120

Annotation of Course

The concept of computer graphics (CG). CG image creation sequence. Colour models and understanding of colours. CG software. Raster and vector graphics systems. Computer animation. Three-dimensional CG development systems. Creating and texturing 3D objects. Basics of image processing technology. Mathematical foundations of CG. Coordinate systems. Two-dimensional and three-dimensional transformations. Classification of projections. Shading models. Interactive 3D graphics.

Aim of Course

Introduce with the principles of image formation, color theory, modern technologies of raster, two-dimensional and three-dimensional vector graphics

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -0
Practical works -15
Laboratory works – 15
Consultations -0
Individual work -50

Annotation of Course

Module contains the information about general principles of drawing making, standards applying, theoretical basic of
engineering graphics and about computer modeling potentialities.
Students are required to attend at least 80% of the practical and laboratory classes at the scheduled time during the semester. Privalomas minimalus modulio paskait� lankomumas – 50%.

Aim of Course

To present graphical problems solving algorithms, to develop abilities to read and make engineering drawings, adapting theoretical knowledge and according to drawing standards requirements and also to manage computer – aided design tools, to develop spatial imagination.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 15
Consultations -2
Individual work -33
Part-time studies:
Lectures -6
Practical works -6
Laboratory works – 6
Consultations -2
Individual work -60

Annotation of Course

Building Information Modelling (BIM) application using engineering graphics software. Building information modeling computer software, and it’s features for civil engineering. Parametric modeling of building elements and their descriptive information. Preparation of building three-dimensional models. Building element and construction (concrete, metal, wood) detailing using building information modeling tools. Preparation of the drawings. Views arrangement in the drawings. Construction drawing. Material schedules and bill of materials. Building plan, section, elevation formation. Building construction drawings. Reinforced concrete, metal, wood, geotechnical design and detailing drawings. BIM and CAD data management and standards. The building structure design schemes. Plotting graphs and using them in documents.
Students are required to attend at least 80% of the practical and laboratory classes at the scheduled time during the semester.

Aim of Course

To introduce the basic princips of building information model (BIM) and it’s management requirements, using computer-aided design systems. After completing this course will be able to read and understand building construction drawings. To present general engineering and computer graphics fundamentals necessary in civil engineering design. To provide with knowledge how engineering graphics methods are applied in building information modeling (BIM) design.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -0
Practical works -45
Laboratory works – 0
Consultations -2
Individual work -33
Part-time, distance learning studies:
Lectures -0
Practical works -20
Laboratory works – 0
Consultations -2
Individual work -58

Annotation of Course

Drawing devices, tools and accessories. Formation of drawings: standards, scales, lines, fonts. Projection methods, projections of points, lines and planes. Projection of parts into three projection planes; viewing of international holes, cavity; views and their displacement in drawings. Surface modeling. Surfaces intersection and their developments.
Students are required to attend at least 80% of the practical and laboratory classes at the scheduled time during the semester. Mandatory minimum attendance of module lectures – 50%.

Aim of Course

To present graphical problem-solving algorithms, to develop abilities to read and make engineering drawings, adapting theoretical knowledge and taking into account the requirements of drawing standards; and also to manage computer- aided design tools, to develop spatial imagination.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 15
Consultations -2
Individual work -33

Annotation of Course

Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. In many cases this methodology facilitates the work of various organizations. In this course, we want to show you how easy it can be to build machine learning solutions with theoretical background yourself and how to do best it.

Students must attend at least 60% of the time scheduled practical works and 50% of the lectures.

Aim of Course

Is to introduce to:
-the main types ant methods of machine learning to solve real problems;
-theoretical foundation of methods, their application possibilities (adventages and disadventages);
-Python (or R) software capabilities to solve these challenges.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -2
Individual work -33

Annotation of Course

This course introduces students to fundamental concepts in Discrete Mathematics, providing a solid foundation for understanding and solving problems in various areas of computer science, information technology, and engineering. The course begins by exploring the basics of mathematical logic, covering logical operations, truth tables, logic formulas, and the fundamental laws governing logical reasoning. Next students will acquire the knowledge and skills to analyze Boolean functions and comprehend functionally complete sets of Boolean functions. The course extends to the realm of sets and relations, examining properties of binary relations, composition, and transitivity. Equivalence and order relations are introduced, providing a foundation for more advanced concepts. The course further includes basics of graph theory, focusing on undirected simple graphs, graph measures, and metrics. Additional attention is given to stable sets, Eulerian and Hamiltonian cycles, emphasizing their significance in practical applications.

Students must attend at least 60% of the time scheduled practical works and 50% of the lectures.

Aim of Course

To acquaint with the main concepts and tasks of mathematical logic, set theory, relation theory, graph theory and methods for solving them. To develop the ability to solve classical problems in discrete mathematics.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -15
Laboratory works – 0
Consultations -2
Individual work -48

Annotation of Course

Integral calculus of functions of one variable: anti-derivatives, indefinite and definite integrals, the basic definitions, methods of integration, the properties of indefinite and definite integrals, Newton-Leibnitz formula, applications of definite integrals, multivariable functions, double and line integrals, ordinary differential equations and their solutions.

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

The aim of this course is to provide the knowledge from integral calculus, the theory of differential equations and series elements, to achieve the ability to analyze situation, to choose the appropriate problem solving method, to present and clarify the obtained results, to develop the ability use the knowledge and practical abilities in the study process.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96
Part-time, distance learning studies:
Lectures -18
Practical works -8
Laboratory works – 8
Consultations -4
Individual work -122

Annotation of Course

Integral calculus of functions of one variable: anti-derivatives, indefinite and definite integrals, the basic definitions, methods
of integration, the properties of indefinite and definite integrals, Newton-Leibnitz formula, applications of definite integrals,
multivariable functions, double and line integrals, ordinary differential equations and their solutions.

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works (full-time studies and part-time, distance learning studies) and 50% of the lectures (only full-time studies).

Aim of Course

The aim of this course is to provide the knowledge from integral calculus, the theory of differential equations and series
elements, to achieve the ability to analyze situation, to choose the appropriate problem solving method, to present and clarify
the obtained results, to develop the ability use the knowledge and practical abilities in the study process.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96
Part-time studies:
Lectures -18
Practical works -12
Laboratory works – 8
Consultations -6
Individual work -116
Part-time, distance learning studies:
Lectures -20
Practical works -20
Laboratory works – 10
Consultations -0
Individual work -110

Annotation of Course

Integral calculus of functions of one variable: anti-derivatives, indefinite and definite integrals, the basic definitions, methods
of integration, the properties of indefinite and definite integrals, Newton-Leibnitz formula, applications of definite integrals,
multivariable functions, double and line integrals, ordinary differential equations and their solutions.

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

The aim of this course is to provide the knowledge from integral calculus, the theory of differential equations and series
elements, to achieve the ability to analyze situation, to choose the appropriate problem solving method, to present and clarify
the obtained results, to develop the ability use the knowledge and practical skills in the study process.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -2
Individual work -98

Annotation of Course

In this course covers the basics of the Big Data, the main big data processing technologies are introduced. During the course the main tools for big data analysis are overviewed. The integration of Big Data systems with other traditional systems is introduced.

Students must attend at least 50% of scheduled lectures and 80% of laboratory works.

Aim of Course

Dalyko tikslas yra suteikti pagrindines �inias apie did�iuosius duomenis (Big Data) bei j� apdorojimo technologijas.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 30
Consultations -4
Individual work -96

Annotation of Course

Discrete mathematics is a branch of mathematics that studies discrete structures and discrete mathematical models of real phenomena. It emerged as an independent field in the middle of the 20th century due to the emergence of cybernetics and computer science and the development of computer technology. Discrete mathematics deals with combinatorics, graph theory, mathematical logic, information coding, finite automata, formal grammars, etc. It also covers issues of number theory, computational mathematics, probability theory, and mathematical programming. Discrete mathematics is important in informatics and cybernetics and has practical application possibilities in algorithms, data storage and transmission, and information processing.

Students must attend at least 60% of the time scheduled practical works and 50% of the lectures.

Aim of Course

To acquaint with basic concepts of graph and number theory, complexity of algorithms, informatikon theory. Students must be able solve typical problems, apply modern mathematical methods to solve real life problems.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -45
Practical works -30
Laboratory works – 0
Consultations -4
Individual work -81

Annotation of Course

The module of study subject introduces parametric and nonparametric hypothesis testing tasks, statistical analysis of dependent observations, correlation analysis. Multivariate statistical analysis methods: regression analysis, factor analysis and classification procedures.

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

The aim of the Applied Statistics subject is to master the basic principles of mathematical statistics and methods of multidimensional statistical analysis, to apply these methods effectively using a specialized statistical data analysis program, to formulate statistical conclusions and interpret the obtained results.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96

Annotation of Course

Stages of decision-making. Data management. Working with electronic spreadsheets. Regression analysis. Forecasting. Optimization Models. Multi-criteria decision making. Decision making under uncertainty.

Students must attend at least 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

To familiarize students with the main decision making methods, work with electronic spreadsheets, to develop the ability to compile mathematical models of real processes, apply modern mathematical methods in decision making tasks.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -15
Practical works -0
Laboratory works – 15
Consultations -2
Individual work -48

Annotation of Course

The Ordinary Differential Equations course consists of general theory of ordinary differential equations and their applications for modelling various real world processes. The basic topics: deffinitions, various first order differential equations (ODE) and their solutions, existence and uniqueness theorems, phase space, direction field, various higher order differential equations and their solutions, fundamental solutions system, systems of differential equations and analysis, a qualitative approach in the plane, the applications of differential equations and systems of differential equations (creating mathematical models, problem solving and analysis).

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

Differential equations course is designed to provide knowledge Ordinary Differential Equations and their systems, relating it to the various application areas, to develop the ability to explore some real processes and to describe them using the differential equations, get the results, analyze and interpret them.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96

Annotation of Course

The Integral Calculus course consists of calculus and application in geometry and mechanics of definite, double, triple, line, surface integrals. Scalar and vector field. Numerical, functional and power series. Application of series in approximate computing.

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

Integral Calculus course is designed provide theoretical and practical knowledge for following studies and application of this courses for solution of the practical problems. To provide knowledge about scalar and vector fields, their types and corresponding application of the integral calculus. Numerical, functional and power series, application in approximate
calculus. To develop possibilities to explore real problems and interpret a given results.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96

Annotation of Course

Examples of algorithms. The main data structures and their implementation. Data search and sorting algorithms. Basic principles for development of algorithms. Complexity of algorithms. Greedy algorithms, dynamic programming. Algorithms for solution of graph problems.

Students must attend at least 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

To introduce data structures, to present principles for development and implementation of algorithms, to show how the complexity of algorithms is analyzed, to solve sorting, search, combinatorial, graph problems.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -45
Practical works -0
Laboratory works – 15
Consultations -4
Individual work -96

Annotation of Course

Theory of realtions. Equavalence relations. Classes. Order relations. Functions. Injective and surjective functions. Graph theory. Path in graphs. Eulerian and Hamiltonian graphs. Graphs isomorphisms. Undirected and directed graphs. Complexity of algorithms. NP completenness. Information theory.

Students must attend at least 60% of the time scheduled practical works, 80% of the time scheduled laboratory works and 50% of the lectures.

Aim of Course

To acquaint with basic concepts of combinatorics, relations, graph and algorithm theory. Students must be able solve typical problems, apply modern mathematical methods to solve real life problems, to modify and generalize formulation of problem.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -4
Individual work -96