Courses taught in English for 2026-2027 Spring Semester

Annotation of course

Object of the reliability theory, main conceptions and definitions. A methodology of the reliability theory. Basic probabilistic and statistical (empirical) indexes of the reliability of the mechanical systems and basic random variables. Calculations of these indexes. Calculation of reliability of the complex repairable and non-repairable technical systems. Reliability of the structural elements and main calculation methodologies.

Aim of course

To obtain acquainted with the reliability theory science, its purposes, methods of estimation of technical objects reliability, of the reliability improvement ways.

Distribution of course hours

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

Annotation of Course

Main path of technical diagnostic is non-destructive testing. Features of non-destructive testing experiments and faults cases, applications, methods and equipment used. Eddy current, magnetic particle, liquid penetrant, ultrasonic, visual, optical methods conception and opportunities used in aircrafts non-destructive testing design.
Students are required to attend at least 50 percent of lectures (continuous studies I stage and integral studies I and II courses), at least 80 percent of laboratory works during the time specified in the schedule.

Aim of Course

Provide theoretical & practical background of aircrafts technical diagnostics, integrating the knowledge of aircrafts construction and inspections, learn to apply these in solving & analyzing practical problems, to understand importance of aircrafts non-destructive testing flight safety.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -0
Laboratory works – 12
Consultations -4
Individual work -120

Annotation of Course

Heating systems using solar energy. Photo electricity and solar power plants. Wind power plants. Theory of flow machines. Steam and gas turbines. Hydraulic turbines. Thermodynamic processes bound with gas compression. Piston, rotary, screw compressors. Turbine compressors. The physical basics of creation of low temperatures. Compression refrigerating machines. Gas absorption refrigerating machines. Thermoelectric refrigerating equipment.

Aim of Course

To provide knowledge of the principles of operation of various machines for energy transformation, the peculiarities of their structures, the opportunities of their use in various branches of industry and their integration in technological processes as well as the trends of their evolution.

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, distance learning studies:
Lectures -25
Practical works -12
Laboratory works – 10
Consultations -0
Individual work -33

Annotation of Course

Design of cutting tools. Calculation and design of specialized cutting-edged tools. Characteristics and use of abrasive tools. Design schemes of specialized devices, their use, design elements of devises, calculation and design of devices, accuracy securing.

Aim of Course

Acknowledge with design schemes, materials, use, calculation and design of tools and devices, theory of their quality achievement.

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 -25
Practical works -10
Laboratory works – 10
Consultations -0
Individual work -115

Annotation of Course

Materials processing technologies: cutting (turning, milling, drilling, counterboring, countersinking, reaming, tapping, broaching), additive technologies (3D printing), machining by abrasion (grinding, finishing, modern abrasion processes
(ultrasonic machining, abrasive jet machining, abrasive waterjet machining, magnetic abrasive machining, machining by
erosion (electrochemical machining, laser beam machining, electron beam machining, plasma beam machining, EDM). Students are required to attend at least 50% of the scheduled lectures and 60% of the practical classes.

Aim of Course

To acquire knowledge about traditional mechanical and non-traditional and innovative materials processing technologies.
Form the ability to correctly select materials and production technologies, to get to know some of the processing operations
in practice.

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
Part-time, distance learning studies:
Lectures -14
Practical works -12
Laboratory works – 0
Consultations -0
Individual work -54

Annotation of Course

Engineering experiment, errors and uncertainties of experiment, selection of variables, continuity of experiments and plan of experiment, statistical analysis of data, computer packages, control and analysis of experiment.

Aim of Course

To acknowlege with fundamentals of experiment planing, give knowledge about correlation of the real experiment and theoretical generalization and also about computerized data processing. To acquire the competence to compare theoretical and experimental results.

Distribution of course hours by study forms and ways

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

Annotation of Course

Fundamental of accuracy achievement in assembly. Methods of assembly organization. Technological preparation. Division of an equipment into units, junctions, subjunctions. Assembly and control of typical connections and junctions. Mechanisation and automatization of assembly works. Comparison of ekonomical efficiency of clifferent processes of assembly. Participation in practical works at least 60% of the scheduled exercises is mandatory.

Aim of Course

To provide knowledge of various equipment assembling technologies as well as devices and means usable.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -12
Laboratory works – 0
Consultations -2
Individual work -42
Part-time, distance learning studies:
Lectures -20
Practical works -10
Laboratory works – 0
Consultations -0
Individual work -50

Annotation of Course

The module provides knowledge of various engineering materials which possess special properties and have particular applicability in mechatronics. Such us conductive materials (heat, electric current, light), insulation (noise, electric power, heat), dielectric, friction, anti-friction, lubrication materials and so on. The module covers material evaluation and selection criteria in mechatronics.

Aim of Course

To provide knowledge about materials used in the mechanical and production engineering in mechatronics, their processing, properties and application.

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 -8
Practical works -0
Laboratory works – 6
Consultations -2
Individual work -64

Annotation of Course

At the end of the course, students will get knowledge on innovation development and its impact on society and economic growth. They will learn the basics of firm theory; will understand innovation in the context of digital economy development, relationships between innovation and R&D. The students will also learn linear and nonlinear innovation development models such as Stage-gate, Toyota/Oobeya, Design thinking, System innovation, technological products development methods and will obtain the skills how to use them for innovation development. The module presents innovation development strategies, concept of open innovation, crowdsourcing, networking, creativity flow theory for innovation development, ideas generation and evaluation methods, the Progress principle. The students will learn how to develop product innovations, to prepare innovation projects and to create prototypes.
Students are required to attend at least 50% of scheduled lectures.

Aim of Course

The course aims to provide students with the knowledge and skills necessary for innovation development. Students will learn patterns and peculiarities of innovation management, will be able to analyze innovation activities, open innovation processes, and will be able to develop organization’s innovation strategy and perform tactical solutions.

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

Mechanical elements, structure, principles of design, components of mechanical systems. Constituent parts of manipulators, their structure and composition principles. Determination of positioning accuracy. Direct and inverse kinematic problems of manipulators, the velocity problem, problems of static forces, dynamic problems. The mechanical systems, their execution systems and their strength calculations. Grippers and principles of their calculations. Sensors, control systems.

Aim of Course

To get acquainted with the structure of mechanical systems, their constituent parts, methods of estimation of the mechanical part of system during the analysis of mechanical system operation. To get acquainted with simulation of mechanical systems.To get acquainted with the structure, principles of their creation, calculations, which are peformed during the designing, sensors used in systems, actuators, control systems. To present information.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 0
Consultations -15
Individual work -245

Annotation of Course

Rudiment of metrology and measurement theory. The principles of statistical measurement theory. Transformation of
measurement signals. Measuring instruments and methods. Measurement of process parameters. Students must attend at least 50% of the lectures, participate in 80% of the practical sessions, and complete all laboratory work.

Aim of Course

To get acquainted with the theory and practice of measurements. General knowledge of the technologies required in
mechanical engineering. knowledge of the laws of engineering mechanics, the principles of applied mechanics, designing of
mechanisms, appliances, devices and apparatus, mathematical methods and laws applicable for description, analysis and
designing of the objects of production in mechanical engineering.

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 -14
Practical works -10
Laboratory works – 10
Consultations -4
Individual work -122
Part-time, distance learning studies:
Lectures -22
Practical works -10
Laboratory works – 10
Consultations -0
Individual work -118

Annotation of Course

The material properties of ecological assessment. Clean production process for the design of new facilities. Material and energy cost reduction in the production process. Device design optimization, reducing their performance and recovery costs. Understanding of the technical system, and the human role in it. Participation in laboratory works in all scheduled exercises is mandatory.
Understanding about technical system and human role in it. Social result of automation. Automation influence to technologic processes. Peculiarities of design of technologic processes. Robots and their role in manufacturing. Automation of technologic processes. Students are required to attend at least 60% of lectures, participate in 75% of practical sessions, and complete 100% of laboratory work.

Aim of Course

Learn the rational use of material and energy resources in the design of new products that their operation would have the lowest impact on the environment, using the minimum amount of energy.
To get knowledge about automation of technical systems and processes, to learn to analyze, to choose and to design the means of automation.

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 -25
Practical works -10
Laboratory works – 10
Consultations -0
Individual work -115

Annotation of Course

The human resource management and leadership course presents the theoretical models of human resource management (HRM), stages and principles of HR strategy formation. The relationship between HRM strategies and organizational culture & business ethics is revealed. Issues of individual and collective behavior of employees are discussed. The aspects of group and team work in an industrial company, the formation and management of an innovation team, the issues of employee integration in the company, group, and team, as well as the manager’s functions, role, and leadership competencies are examined. The concept of the phenomenon of leadership is presented, the differences between leadership and management and the relations of interaction are identified. While examining the theoretical aspects of leadership, leadership styles are overviewed, the principles of practical applicability of leadership competencies and skills are revealed.

Aim of Course

To provide academic knowledge and abilities to apply human resource management and leadership in manufacturing practice, consultation activity, academic researches.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -45
Practical works -0
Laboratory works – 0
Consultations -0
Individual work -115

Annotation of Course

Study course covers simulation, models of discrete and random processes, forming principles of random process models on the samples of computer simulation by the use of “Arena” program facilities, comparing of model and system structure, analysis of results, machining operations simulation, safeguarding of their productivity simulation and optimization, procedures and basic principles of work flow of manufacturing companies, general economic and manufacturing KPI’s, compatibility of information systems of production management with modern production strategies, basic rules and planning of technological resources and human resources, production management decisions’ economic evaluation and modeling, integration of sales, supply chain and financial procedures into manufacturing.

Aim of Course

To acknowledge with principles of process, especially random, simulation and with manual and computer simulation, and simulation programs. Acknowledge with different manufacturing processes simulation, their features and optimization. Introduce with objectives, tasks, designing of information systems of production management also with enterprise resource planning system.

Distribution of course hours by study forms and ways

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

Annotation of Course

Marketing of innovative products and investment management course covers innovative product idea generation and initial business model creation using Strategyzer Canvas model, marketing of innovative products and its process overview, industrial activities and competitors analysis, quantitative and qualitative demand analysis, innovative products marketing planning, strategy and development, marketing of innovative products in internet, investment management concept and investment planning, investment appraisal methods and investment climate analysis.

Aim of Course

The module shall provide knowledge and develop students’ skills in innovative product marketing and investment management.

Distribution of course hours by study forms and ways

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

Annotation of Course

The basis of tribology and wear processes is presented in the module. Significance and development of tribology, physics of tribological materials and properties of materials surface, working surfaces of parts and their interaction, type of friction and lubrication, wear and its types, friction and abrasion tests, tribotechnical materials and coatings forming methods, methods of surface strengthening and wear prevention, structural methods of increasing wear resistance of parts and lubrication of friction pairs.
Students are required to attend at least 50 percent of lectures (continuous studies I stage and integral studies I and II courses), at least 60 percent of exercises during the time specified in the schedule.

Aim of Course

To familiarize with friction and wear processes taking place in machine assemblies, their regularities, methods of increasing wear resistance of parts and applications of lubricants.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -12
Laboratory works – 0
Consultations -2
Individual work -42
Part-time studies:
Lectures -10
Practical works -8
Laboratory works – 0
Consultations -2
Individual work -60
Part-time, distance learning studies:
Lectures -20
Practical works -10
Laboratory works – 0
Consultations -0
Individual work -50

Annotation of Course

This module presents key concepts and principles of innovation and innovation management process, innovation clasification, creativity stimulation tools and methods, ideas generation and evaluation process, management of innovation develoment team, development of innovation project, sources of innovation financing, innovation comercialisation process, intelectual property rights, licencing. Participation in practical works at least 60% of the scheduled exercises is mandatory.

Aim of Course

The course aims to provide students with the knowledge and skills necessary for innovation development.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -12
Laboratory works – 0
Consultations -2
Individual work -42
Part-time studies:
Lectures -10
Practical works -6
Laboratory works – 0
Consultations -2
Individual work -62
Part-time, distance learning studies:
Lectures -16
Practical works -8
Laboratory works – 0
Consultations -0
Individual work -56

Annotation of Course

The basis of structural materials and treatment processes is presented in the module. Tasks and development of materials science. Mechanical and physical tests of materials properties. Metal production and casting. Forming by extrusion and cutting technologies of materials. Materials joining process and coating technologies. Polymeric, composite and ceramic materials and their technologies. Powder and additive manufacturing technologies. Non-destructive control of materials.
Students are required to attend at least 50 percent of lectures (continuous studies I stage and integral studies I and II courses), at least 80 percent of laboratory works during the time specified in the schedule.

Aim of Course

To provide knowledge about materials used in the mechanical and production engineering, their properties, processing and application.

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 -16
Practical works -0
Laboratory works – 6
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

Conception of quality, prime rates of quality. Terms and descriptions. System of quality and its structure. Requirements and principles of quality system. Control of quality, administration of quality. Methods of quality control. Planning and organization of quality control. Methods of productions presentation for control. Internal audit of quality, its organization and planning. Methodology of quality audit. Concepts and terms of marketing. Requirements of quality for marketing. Quality of marketing. Price of quality and its optimization. Participation in practical works at least 60% of the scheduled exercises is mandatory.

Aim of Course

To give knowledge about terms of quality and indexes, systems, control of quality, administration of quality and quality of marketing. To determine what is dependence between quality of product and price.

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
Part-time, distance learning studies:
Lectures -20
Practical works -8
Laboratory works – 0
Consultations -0
Individual work -52

Annotation of Course

Production and technological processes, operations, their division to separate parts. Machine as production object. Specifications of a machine and its parts, their research, methods of quality assurance. Design and rating of technological process of parts production. Evaluation of effectiveness of technological process Processes plans for production of machine parts of different types. Documentation of technological processes. Creation of operation programs.

Aim of Course

To prepare the specialist which according to drawings of machine parts and their specifications would rightly understand processes of their production and itself could create, select, and improve technological processes of their production thanks to which quality of a machine and its parts would be achieved with a minimal cost.

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 -22
Practical works -10
Laboratory works – 10
Consultations -4
Individual work -114

Annotation of Course

Machine design process. Selection of motors. Methods of design of drives. Design of gear boxes. Design of spindles, using various bearings: sliding bearings (hydrodynamic type, hydrostatic type), rolling bearings. Design of summing of motions mechanisms, traction mechanisms. Particularities of design of instruments. Students are required to attend at least 50% of lectures and complete 100% of laboratory work.

Aim of Course

To get acquainted with methods of the design of machines and instruments, allowing to get required force and speed characteristics of the machine, with designing of precise mechanisms, mechanism of summing of motions, traction mechanisms, particularities of mechanisms of instruments. Develop skills to apply this knowledge to the selection of main machine and instrument components and assemblies.

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, distance learning studies:
Lectures -16
Practical works -0
Laboratory works – 8
Consultations -0
Individual work -56

Annotation of Course

The course provides a comprehensive understanding of the fundamental principles, design, operation, and maintenance of various medical devices and equipment used in healthcare settings. This course delves into the technological advancements, safety protocols, and practical applications of a wide range of medical equipment utilized in diagnostic, therapeutic, and monitoring procedures. Students are required to complete all scheduled laboratory work.

Aim of Course

Aims to equip students with an in-depth understanding of medical equipment components, characteristics, and applications, fostering their ability to analyze, operate, and maintain diverse medical devices within healthcare environments.

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

Biosignals are important transmitters of information about a biosystem or its parts. Their diversity and nature define the characteristics of measurement systems and processing methods, as well as the limits of application. In this course, you learn to identify biosignals, measure them, process them and interpret the obtained results. It is learned to critically assess and analyze the information of bioelectrical signals and the possibilities of practical application.

Aim of Course

To introduce the variety of biosignals, methods of measurement, research, and processing, and to learn to apply the acquired knowledge by experimenting and analyzing the presented situations, as well as to be able to critically evaluate the obtained results and to interpret them.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -15
Laboratory works – 15
Consultations -0
Individual work -180

Annotation of Course

The course is dedicated to familiarizing with the features of biofluids, investigating their differences from other fluids, and analyzing biofluid statics and dynamics. During this course, the blood flow is analyzed and modeled. A great attention is paid to the phenomena of the blood flow in the pathological blood vessels.

Aim of Course

To introduce the concepts of biofluids, their properties and differences from other fluids.

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

During this course, design and manufacturing of orthopedic equipment are analyzed. Course introduces orthopedic disorders and their diagnostic methods, classification of orthopedic equipment, its manufacturing stages, tools, machinery and materials used in manufacturing process. Students are required to complete all scheduled laboratory work.

Aim of Course

To provide knowledge about design and manufacturing of orthopedic equipment. To develop ability to use this knowledge while applying, designing or manufacturing such equipment to people with different orthopedic disorders.

Distribution of course hours by study forms and ways

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

Annotation of Course

Technical assistance devices are necessary for the majority of persons with various forms and levels of disabilities, as they become an integral part of life, and also provide independence and psychological comfort. During this course, you get to know not only the variety and availability of technical aids, but also their technical characteristics, individual application, and also design features. It is learned to select or design technical support measures according to individual situations.

Aim of Course

To acquaint students with the diversity of technical aids for the disabled, the principle of operation, application, design and production features, and to provide the ability to apply knowledge when selecting, adapting or designing technical aids for the needs of the disabled.Students must attend at least 60% of the time scheduled exercise.

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

The course introduces the fundamentals of hospital information systems, emphasizing their functionalities and pivotal roles within healthcare institutions. It explores the integration of computer networks in healthcare settings, focusing on infrastructure standards and electronic health fundamentals. Moreover, the course covers the implementation and management of electronic patient records, collection, and processing of clinical data via application of AI-based algorithms, and modern technologies shaping healthcare information systems. Students must attend at least 60% of the time scheduled exercises.

Aim of Course

Aims to equip students with an understanding of healthcare information systems, their applications, and the technologies shaping modern healthcare delivery

Distribution of course hours by study forms and ways

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

Annotation of Course

The subject is intended to introduce students to the requirements for materials used in medicine, to provide an understanding of the selection of materials for implants and prostheses, and develop the ability to independently analyze the biocompatibility, durability and other problems of materials in medicine. Students must participate in at least 70% of the exercises during the scheduled time.

Aim of Course

To introduce students to the classification and properties of biomaterials (polymers, metals, bioceramics, carbon fiber, new nanomaterials), to provide an understanding of material selection for implants and prostheses, to ensure that students are able to identify and solve material selection problems

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

The subject is designed to introduce students to the regulation of medical devices in the EU, to provide an understanding of the life cycle of medical products, biocompatibility testing, in silico trials of medical devices, reliability and risk management methods of medical devices, quality assurance, to introduce the ability to independently analyze statistical methods of diagnostic theory and new medical diagnostics method.

Aim of Course

To introduce students to the regulation of medical devices in the EU, to provide an understanding of quality assurance, reliability and risk management of medical devices, to ensure that students know and can apply statistical methods in diagnostics, to provide knowledge about new medical diagnostic methods.

Distribution of course hours by study forms and ways

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

Annotation of Course

Calculation correction, selection of the schemes and the workflow. In design final works are made 4 of A1 size drawings – general view, assembly drawings of separate mechanisms and drawings of parts. In technological final works are made 4 of A1 size schemes of the technological processes and economical evaluation. Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

To learn to write the Project theoretical part, to make technical calculations, to draw correctly drawings, present and define them.

Annotation of Course

Aim of Course

To learn to write the Project theoretical part, to make technical calculations, to draw correctly drawings, present and define them.

Distribution of course hours

Full-time studies:
Lectures -0
Practical works -12
Laboratory works – 0
Consultations -4
Individual work -144
Part-time studies:
Lectures -0
Practical works -8
Laboratory works – 0
Consultations -8
Individual work -144

Annotation of Course

Aim of Course

Introducing students with elements of mechatronic systems and their interaction. Joint of components of different physical nature to united mechatronic systems.

Distribution of course hours

Full-time studies:
Lectures -30
Practical works -0
Laboratory works – 15
Consultations -2
Individual work -33
Part-time studies:
Lectures -8
Practical works -0
Laboratory works – 8
Consultations -8
Individual work -56

Annotation of Course

The module introduces the methods of technical solution analysis and reliability assessment. The concept of quality and artificial intelligence based methods of its assurance is introduced as well.
Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

The module aims to introduce the methods used to analyse technical solutions and to introduce practices of mechatronic and robotic systems research and their reliability assessment

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

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 aim of the course includes basic language processing technologies and their methodologies. The possibilities and limitations of the application are introduced. Aims to advance knowledge of natural language processing using advanced natural language techniques and technologies.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

Aim of Course

The “Basics of Language Processing” course is designed to provide students with an in-depth understanding of the various technologies and methodologies used in natural language processing. This course covers both theoretical concepts and practical applications of language processing technologies, and students are expected to gain proficiency in using relevant tools and techniques for processing human language. Some of the topics covered in this course include linguistic analysis, text representation, language models, statistical and rule-based approaches, deep learning, and natural language generation. Students will also be introduced to various applications of language processing technologies such as sentiment analysis, machine translation, question answering, and chatbots.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -24
Practical works -0
Laboratory works – 12
Consultations -2
Individual work -42

Annotation of Course

This comprehensive course on machine learning is designed for beginners who want to develop a solid foundation in the field. The course covers a wide range of topics, from the basics of machine learning, to pre-processing and feature selection, to building different types of models, and even delving into advanced topics like natural language processing. Each lesson provides a clear explanation of the topic, followed by examples and exercises to reinforce the concepts learned. The course also emphasizes the importance of evaluating and validating models, as well as best practices and tips for success.
By the end of the course, students should have the skills and knowledge necessary to apply machine learning algorithms to real-world problems and continue their studies in the field.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

The aim of this course is to provide beginners with a comprehensive understanding of machine learning, including its different types, pre-processing and feature selection, building models, and advanced topics. By the end of the course, students should be able to apply machine learning algorithms to real-world problems and have a solid foundation for further study in the field. The course also emphasizes the importance of evaluating and validating models, as well as best practices and tips for success, to help students develop a complete skillset in the field. Ultimately, the goal of this course is to provide students with the knowledge and skills necessary to become proficient in machine learning and begin applying it to solve 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

The Systems and Devices of Applied Artificial Intelligence course explores the intersection of artificial intelligence and mechatronics. It aims to equip students with the knowledge and skills required to design, develop, and implement intelligent systems and devices that integrate with mechanical and electrical components.

Mechatronics, which is the combination of mechanics, electronics, and computer science, plays a crucial role in the implementation of intelligent systems. The course delves into the fundamentals of mechatronics, including sensors, actuators, control systems, and signal processing. It also covers various topics related to artificial intelligence, including machine learning, computer vision, natural language processing, and robotics.

The main aim of the course is to provide students with a comprehensive understanding of the theoretical and practical aspects of integrating artificial intelligence and mechatronics. Students will learn how to apply artificial intelligence techniques to real-world problems in various fields, including manufacturing, healthcare, transportation.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

Aim of Course

The aim of this course with a course project is to provide students with an opportunity to apply the knowledge and skills they have acquired throughout the course to a practical project. The course aims to enable students to design, develop, and implement an intelligent system or device that integrates with mechanical and electrical components, utilizing the concepts and techniques of artificial intelligence and mechatronics.
The course project provides a platform for students to apply their theoretical knowledge to real-world problems, to gain hands-on experience with the tools and technologies used in the industry, and to develop critical thinking and problem-solving skills. It also helps students to develop teamwork, communication, and project management skills, as they work collaboratively with their peers to complete the project within the given timeline and budget.
The aim of the course project is to enable students to gain practical experience in the integration of artificial in

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -45
Practical works -30
Laboratory works – 15
Consultations -6
Individual work -144

Annotation of Course

This module provides a comprehensive introduction to deep learning, a subfield of artificial intelligence that involves the use of neural networks to learn and make decisions. The module begins with an overview of linear models, which are used to make predictions based on a set of input variables. It then introduces the concept of information and maximum likelihood, which are used to estimate the parameters of a statistical model.
Next, the module covers logistic regression, a widely used statistical technique for predicting the probability of an event. It also covers modular backpropagation, a method for training neural networks by adjusting the weights and biases of the network in response to errors in the output.
In addition to these foundational concepts, the module also explores more advanced topics such as convolutional neural networks, which are used for image recognition and processing; max-margin learning, a method for training support vector machines; and recurrent neural networks, which are used for tasks involving sequential data.
The module also covers the use of game theory in deep learning, as well as best practices for designing and implementing neural networks. It concludes with a discussion of variational autoencoders and image generation, as well as the use of deep reinforced learning and neuro-dynamic programming in artificial intelligence.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

Aim of Course

The aim of this module is to provide a comprehensive introduction to deep learning, including both foundational concepts and advanced topics. It covers a wide range of techniques and applications. Through this module, students will gain a strong foundation in deep learning and be well-prepared to tackle more advanced topics in the field.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -45
Practical works -30
Laboratory works – 0
Consultations -6
Individual work -159

Annotation of Course

The advancement of digital technologies in the context of the fourth industrial revolution is largely driven by artificial intelligence (AI) as a key technology. The abundance of data, powerful algorithms, and the significantly more powerful field of computing and the Internet of Things (IoT) are influencing social and political aspects of life. Mechatronic systems, robotics, digitization are significantly changing and will continue to change various aspects of our lives. This Applied Artificial Intelligence Tools and Technologies course will cover the fundamentals of AI, the tools used to achieve human-specified goals with some autonomy of action. This course is a comprehensive undergraduate introduction to artificial intelligence, covering the context of fields such as technology, ethics, law, statistics, economics, technology and the social sciences, with touches on the arts, natural sciences, medicine and health sciences.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

Aim of Course

To introduce various technologies applied to artificial intelligence, to learn how to apply various advanced reinforcement learning algorithms, tools (such as TensorFlow, Caffe, Keras, PyTorch, Auto ML, H20: Open-Source AI Platform, etc.) to any problem

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

To introduce students to engineering programming capabilities, the internal data structures of a programming language, and basic algorithms for processing them. To teach students to write engineering programming applications, test them, and prepare their documentation.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

To provide every major C++ language feature and the standard library facilities, to get understanding of the ideas presented in related lectures behind the language facilities, leading to mastery.

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 -0
Laboratory works – 6
Consultations -8
Individual work -138

Annotation of Course

Continous time and discrete time systems, control algorithms, direct and closed loop control, system analysis, Laplace, Fourier and “z” transforms, signals, adaptyve control, neutral networks, computer based data acquisition and control devices.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

Aim of Course

To give knowledge on mechatronical systems control theory, signals and control implementation using computer (controler) based data acquisition and control systems, to explain the methods of control systems analysis and synthesis, to give knowledge on adaptyve control systems and neural networks.

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
Part-time studies:
Lectures -30
Practical works -15
Laboratory works – 0
Consultations -10
Individual work -105

Annotation of Course

Overview of computer aided design and manufacturing. Computer aided design (CAD) and computer aided manufacturing (CAM) components, hardware and software. Concept of CAD: three-dimensional (3D) modeling, virtual modeling, integrated quality control. Interface between CAM and CAD. Fundamentals of CAM. Computer aided engineering.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

Implementation of modern 3D systems for geometric form generation, construction parameters analysis and preparation of computer aided manufacturing.

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 -10
Practical works -12
Laboratory works – 0
Consultations -8
Individual work -130
Part-time, distance learning studies:
Lectures -22
Practical works -16
Laboratory works – 0
Consultations -0
Individual work -122

Annotation of Course

The module introduces the concept of quality, the quality assurance system and its components and the functioning of the quality management process.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

To get acquainted with the system of quality management and automated production conditions

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 studies:
Lectures -10
Practical works -6
Laboratory works – 8
Consultations -10
Individual work -126

Annotation of Course

Machine elements. The criteria of operation. Joints of elements. Welded, pressed, riveted, glued joints. Thread, wedge-shaped, splined, pin joints (connections). Friction drives. Belt drives. Gear drives: cylindrical and conical gear drives. Worm gear drives. Shafts, bearings, couplings, sealing and lubricating equipment. Laboratory works. The course project.

Students must participate in at least 75% of the lectures, exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

Introduction to machine elements and various joints of them.

Distribution of course hours by study forms and ways

Full-time studies:
Lectures -30
Practical works -30
Laboratory works – 15
Consultations -4
Individual work -81
Part-time studies:
Lectures -18
Practical works -8
Laboratory works – 8
Consultations -4
Individual work -122
Part-time, distance learning studies:
Lectures -30
Practical works -10
Laboratory works – 10
Consultations -0
Individual work -110

Annotation of Course

Methods of modelling. Analysis of mechatronic systems on basis of their modelling possibility. Modelling of components of mechatronic systems. Jointed mechatronic systems, influence of control. Synthesizing of new mechatronic systems with desired properties in case of modelling new systems.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

Aim of Course

Introducing students with various methods of mechatronic system modelling.

Distribution of course hours by study forms and ways

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

Annotation of Course

Provides information about the structure of mechatronics systems, methods of structure investigation and diagnostics of failures. Provides an overview of diagnostic equipment, introduces the circumstances of potential fault location of mechatronic systems. A description of the course of diagnostic testing of mechatronic systems is provided.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

To deliver knowledge about structure of mechatronic systems, to introduce with diagnostic equipment and their implementation methods of such systems.

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

There are delivered detailed overview of sensors in mechatronics. Detailed description of their design features, methods and possibilities of their implementation in mechatronics. There are discussed possibilities of sensor design and enhancement.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

To introduce with design features of sensors and their implementation in mechatronics.

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

Introduction. Main tasks. Practical and economic aspects. Areas of application. Micro robotics. Nano systems. Widely used robotic systems and specific of application. Optical recognition tasks and applications. Methods of robotic systems integration. Programming of industrial robots. Languages and systems. Levels of robot programming. Problems related with robot programming. Recommended solutions. Off-line industrial robotic programming systems. Technical requirements for CAD/CAM models. Practical aspects of TCP control scenario. Tool control task. Commonly used robotic systems. Technical and practical aspects robotic systems. Commonly used robotic system control software. Recommendations for practical applications.

Aim of Course

To familiarize students with the practical application of industrial robots, typical robotic solutions and the latest software packages used for these purposes.

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 – 8
Consultations -18
Individual work -118

Annotation of Course

There is an intention to introduce general methods of design of machines and equipment. To introduce with design of mechatronic systems, selection of components, design of control systems. Overview of program driven system types, and their design, balance between software emulated and hardware released elements in mechatronic systems. Modernization of old mechanical equipment by implementing modern control systems. Choose of rational depth of modernization.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the
scheduled time.

Aim of Course

Introducing students with design process of mechatronic and robotic systems : methods of design of frames, drives, power elements and sensors.

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
Part-time studies:
Lectures -10
Practical works -8
Laboratory works – 0
Consultations -10
Individual work -132

Annotation of Course

Overview of mechatronic systems control, types of controllers. Different types of mechatronic systems. Synthesis of desired properties in mechatronic system design. Parameters of control of different types of mechatronic systems, dynamics of mechatronic system control. Design of real systems using digital modeling without solving differential equations analytically.

Students must participate in at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Aim of Course

Introducing students with control of mechatronic systems. Control of discreet, continuous and proportional 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 -12
Practical works -12
Laboratory works – 0
Consultations -8
Individual work -128