Applied Artificial Intelligence

Module aim

To familiarize with the use of modern 3D design systems to create the geometric shape of the designed products, to determine the structural parameters of the systems, to prepare for automated production; develop the ability to read and create engineering drawings using modern computerized design systems and drawing standards; learn how to create parametric models of parts and combinations of parts and generate drawings using the automated three-dimensional design system SolidWorks.

Module description

Basic concepts of computer-aided design (CAD) applied to mechanical engineering design problems. Digital methods, computer graphics, geometric modelling, design optimization. Overview of Automated Design and Manufacturing Process Automation. Structure of automated design and production system; hardware and software. Automated design concept: three-dimensional (3D) model, virtual simulation, integrated design quality control. The relationship between automated manufacturing and the design process. Basics of automation of production processes. Development of engineering systems. Drawing of sketches, measuring tools, marking of materials in drawings. Assembly drawings and their drawing order. Rendering of drawings; design methods; images and their location in drawings; cuts; volumetric modelling according to axonometry and according to the given two views. Surface modelling. Designation of types of surface treatment in drawings.

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

Module aim

Studying mechanics involves gaining a deep understanding of various aspects. Firstly, it is important to grasp the fundamental principles of statics and dynamics, which govern the behavior of mechanical objects under the influence of forces. By exploring the general notions of mechanics and employing solution methods for statical, kinematical, and dynamical problems of rigid body mechanics, one can develop the necessary skills to solve practical problems in this field. Acquiring knowledge about the behavior of mechanical objects under known boundary and initial conditions is crucial in this process. By approaching mechanics in a systematic and study-oriented manner, one can effectively acquire and assimilate the required knowledge.

Module description

This course provides a comprehensive study of mechanics, focusing on the fundamental principles and concepts. It covers the object of mechanics, including idealizations and the fundamental axioms, laws, and notions that form the basis of this field. The course explores particles, rigid bodies, and mechanical systems, as well as the concepts of force, couple, moment, and link. It delves into forces in both two-dimensional and three-dimensional space, emphasizing the importance of free-body diagrams. The equilibrium of particles and rigid bodies is examined, along with the consideration of distributed loads and the concept of the gravity center. The course also covers the topic of friction.In addition to statics, the course introduces the basic principles of kinematics, including velocity, speed, acceleration, and path. Equations of motion are discussed, and the study progresses to kinetics, addressing the motion of particles and rigid bodies. Differential equations of motion are explored, along with the fundamental theorems of kinetics. The course also provides an introduction to analytical mechanics. To successfully complete the course, students are required to attend a minimum of 70% of the scheduled practical exercises and 80% of the laboratory works. This ensures active engagement and practical application of the concepts learned throughout the course. Theoretical lectures are mandatory for first-cycle I-III year full-time students. More than half of the lectures must be attended during the semester.

Module aim

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

Module description

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, 80% of the time scheduled laboratory works and 50% of the lectures.

Module aim

o provide knowledge about materials used in the aircraft manufacture, their processing, properties and application.

Module description

The basis of engineering materials and treatment processes is presented in the module. Materials science development, internal structure of materials, mechanical and physical properties testing, patterns of the formation of material structures, production of metal materials, alloy steels and non-ferrous metal alloys, heat treatment and thermochemical treatment of metal materials, composition and non-metallic materials, metal corrosion and protect, details and construction units manufacturing and processing methods, materials and components joining process, destructive and non-destructive control of materials.

Module aim

The aim of this course is to provide foundational understanding of optical physics, laser systems and photonic technologies, while equipping them with practical knowledge to integrate optical data and systems in to artificial intelligent driven applications.

Module description

This course introduces the fundamental principles of optics and the operation of lenses, optical systems. It then explores the physics and engineering of lasers. Beyond core physical concepts, the course connects optics with applied artificial intelligent using machine learning models. Students will gain practical experience through mini-projects applying AI tools to process optical data, enabling them to bridge physical systems with intelligent computing solutions.
Students must attend at least 60% of the time scheduled of the lectures.
Students must attend at least 80% of the time scheduled laboratory work.

Module aim

To introduce the students the importance of individuals’/families’ finance management.
To give the necessary knowledge about personal finance management principles and methods, saving, investment, borrowing possibilities and instruments.

Module description

The course analyzes theory of personal finance, investments and its implementation for individual (family) for decision making in such areas as consumption, saving and borrowing, investments, pension planning, insurance services, purchasing of estate and tax planning.
Explanation of family budgeting, balance sheet and cash flow accounts’ arrangement and estimation, applying of these statements for motivation of financial decisions, assessment of personal financial status, individuals’/families’ financial ratios, possible financial instruments for implementation of long and short term goals, consumption, saving, investment and borrowing decisions are analysed, investment strategy, creation and effective establishment of investment portfolio are studied, insurance services, importance of individuals’ risk insurance are discussed, real estate planning and important tax problems for households are studied.

Students must attend at least 60 per cent of the seminars and at least half of the lectures at the scheduled times.

Module aim

Acquaint with philosophical ethics and fundamental ethical problems and concepts. Transmit a knowledge of ethical foundations, principles and systems. Foster critical judgement and the capacity for logical, reasoned discussion. Encourage a sense of values.

Module description

Students learn about basic ethical schools and systems, fundamental issues of deontological and teleological ethic. Historical developement of ethical thought, periods such as Early Asian, Greece and Romain, medievvial, Reneissance, New Age and modernism. The main ethical issues are discussed: good and evil, principle of morality and free will, person as a goal in itself, notion of dignity, conscience, norm and morality, grounding morals in athority and discourse, notion of virtue, happiness and meaning of life and etc. Analyzed texts and philosophic al arguments os themost significant scholars of the field (Plato, Aristotle, Kant).
Students must attend at least 60 percent of the seminars and at least half of the lectures at the scheduled times

Module aim

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.

Module description

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.

Module aim

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

Module description

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.

Module aim

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.

Module description

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 must attend at least 60% of the time scheduled of the lectures.
Students must attend at least 60% of the time scheduled practical lectures.
Students must attend at least 80% of the time scheduled laboratory work.

Module aim

Provide knowledge about linear DC, AC single-phase and three-phase circuits, transients in linear DC and AC circuits, magnetic circuits. Develop the ability to experimentally investigate and develop these circuit using various methods.

Module description

at least half of the lectures at the scheduled times

Module aim

To provide knowledge and develop skills to be able to analyze problematic situations and look for alternative solutions, assessing possible social and environmental consequences, to familiarize with the solution of safety problems.

Module description

Natural and technogenic hazards, their nature, mechanisms of impact on humans and possible consequences of exposure are examined. Knowledge of reducing the consequences of exposure to humans is provided. Environmental pollution caused by human activity, its sources and consequences of global pollution are analyzed. Knowledge of ways and means of reducing environmental pollution is provided. Students are required to complete all scheduled laboratory work. Theoretical lectures are mandatory for first-cycle I-III year full-time students. More than half of the lectures must be attended during the semester.

Module aim

Main principles of operation, control and composition of robotic systems are examined. Robotic and intelligent automation systems are reviewed, and their mutual interaction is analysed.

Module description

To introduce the principles of operation and application of industrial robots and intelligent automation systems

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

Module aim

Giving information about DBMS, its differences, advantages and disadvantages; to teach how to design a relational database using ERD, how to use SQL language, how too write various queries, and create data entry forms for DB users.

Module description

The aim of the course is to introduce the concept of relational databases, possible actions with the data stored in the database; introduces the database modeling technique and design of database objects. The course also introduces the graphical user development environment and its development features.
Students must attend at least 60% of the time scheduled practical lectures.

Module aim

To introduce to the theoretical foundations and practical applications of scanning and recognition systems. The course aims to provide knowledge of various scanning methods, data processing algorithms, and object recognition technologies. It seeks to develop students’ abilities to design and implement automated systems for analyzing and interpreting information from scanned images or signals. In addition, the course covers color analysis and recognition methods essential for accurate image interpretation and reproduction. The course prepares students to apply these technologies across diverse fields, such as computer vision, biometric solutions, medical diagnostics, industrial automation, and more.

Module description

The course covers various scanning methods, such as image, text, and biometric data scanning, and their application areas. Students will explore technologies such as Optical Character Recognition (OCR), facial recognition, fingerprint recognition, and other modern technologies used in scanning and recognition processes. Color analysis and recognition techniques are introduced as part of the comprehensive approach to scanned image processing. The course includes practical examples and analysis of real-world problems solved using scanning and recognition systems. Data processing, filtering, and accuracy assurance aspects will also be discussed. The course is focused on both theoretical and practical learning, preparing students to work in this rapidly evolving field. Students must participate in at least half of the theoretical lectures, at least 75% of the exercises and complete at least 75% of the laboratory work in the scheduled time.

Module aim

To provide knowledge about decision support methods, their application and benefit for organization, as well as to develop an understanding of the principles of developing intelligent information systems, agent technologies and learn to apply them in practice.

Module description

Decision support is an interdisciplinary discipline where the concepts, methods, and techniques of systems analysis, optimization theory, game theory, theory of service, machine learning are analyzed. Students are introduced to modern intelligent information systems as well as decision support systems that help enterprieses make decisions and to achieve their strategic goals. The subject also examines the practical application of multi-agent systems, illustrates the theoretical material with specific examples of the application of agent technologies, teaches how to break down complex problems into simpler ones. The subject also includes the analysis of agent development problems, their interaction, communication and agent systems modeling.
Students must attend at least 60% of the time scheduled practical lectures. Mandatory minimum attendance of module lectures – 50%.

Module aim

To prepare specialists, which are able in complexity to understand and analyse economical development trends and perspectives in various world regions, specifically in China and able to prepare and implement concrete international projects for development of economical relations with this region.

Module description

Engineering solutions, economic justification of the course introduces the fundamental concepts of engineering and economic principles. In more detail the production process and presents the cost / cost, cost and profit and raising its capabilities. Considered the main project performance evaluation methods in choosing the optimal solution for the implementation of an alternative. Introduction to engineering solutions commercialization opportunities with emphasis on environmentally sustainable development issues.
Students must attend at least 60 % of the time scheduled exercises.

Module aim

To form basis of methodological knowledge for organization management, and educate capabilities to apply an acquired knowledge in examining real professional situations.

Module description

During the course the following topics are covered: essence of management, basic concepts and their interpretations, evolution of management theories, cyber management model: subject and object of management. There is analyzed organization as a system (systemic view application), types of organizations, elements and environment of organization, establishment of organizations and organization’ management types of structures. Also there are analyzed functions of management: planning, organizing, leadership and controlling, administrative and economic as well as psychological methods of management, manager role in the system of organization management, management’ decisions’ preparation and adoption principles, pay systems and motivation. There are disputed change and conflict management. Students must attend at least 60% of the exercises according to the timetable.

Module aim

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

Module description

Examples of algorithms. The main data structures and their implementations. Data search and sorting algorithms. Some basic principles for development of algorithms. Complexity of algorithms. Algorithms for solution of graph problems.
Students must attend at least 80% of the time scheduled laboratory work.

Module aim

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.

Module description

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.

Module aim

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.

Module description

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

Module aim

To get familiar with image analysis systems, methods and algorithms used in them and the basic principles of their implementation using modern software.

Module description

The study subject provides an introduction to image recognition, explaining the analogy of computer vision system and biological vision system operation, image sensor operation and types, image formation and color models. The subject analyzes the methods of image preprocessing, noise reduction, fast image analysis operations, features of color image analysis, image features, geometric transformations of images, creation of panoramic photos. Theoretically and during practical classes, the principles of image classification, object recognition in images, image segmentation are analyzed. The subject also provides knowledge about the advanced principles such as image style transfer, colorization, reconstruction, superexpression and image synthesis. Examples from different applications are provided: face detection and recognition; optical character recognition; recognition of license plates and road signs; detection of objects of interest and determination of their movement in image sequences. During the practical work of the subject, modern software MATLAB, OpenCV, PyTorch, Tensorflow are applied. Students must complete all scheduled laboratory work. Students must attend at least 80% of the course laboratory and at least half of the lectures according to the semester schedule.

Module aim

Gain knowledge of principles, structure, elements and applications of automatic control devices and systems, acquire skills in
experimental investigation of control systems.

Module description

at least half of the lectures at the scheduled times

Module aim

Analysis of the company ‘s structure, used equipment, machine designs, and production. Acquired practical work skills. Collected material for the final thesis during practice. Preparation of practice report.

Module description

Formation of practical work skills in a real working environment. Acquisition and improvement of essential leadership and management skills during professional activities.

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

Module aim

The subject is designed to learn how to apply artificial intelligence to data analysis, decision-making, learn how to create a system prototype, test it and use the information for production. Students perform related laboratory work, participate in group work through practical tasks, create a prototype and present it in a seminar, and participate in discussions.

Module description

Learn to apply artificial intelligence during initial data analysis, interpret the results, use artificial intelligence to apply the appropriate decision-making method, design a prototype of an applied artificial intelligence system, test it and use production resources to produce a product.

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

Module aim

Gaining knowledge about digital automation elements, their structure, operation and use of digital automation.

Module description

at least half of the lectures at the scheduled times

Module aim

Based on the chosen topic of the Bachelor’s final thesis, its structure is created, and the necessary sections are planned. A literature review, variant analysis and existing prototypes are examined, and a technical solution concept is prepared.

Module description

Selection of the topic of the bachelor thesis (project) and formulation of the objectives and tasks of the thesis.

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

Module aim

The previously performed calculations, the selection of schemes, and the course of technological processes are adjusted. Drawings and schemes are being developed, and preparations for the public presentation of the project are being made.

Module description

To learn how to write the theoretical part of the final project, to perform the necessary technical calculations for the project, to correctly form drawings and diagrams, to present and defend the project.

Module aim

According to the assignment of the final work, structural diagrams and logical diagrams of designed mechanisms, devices or systems are drawn up, and the necessary parameters are calculated. Control schemes and algorithms are created.

Module description

To teach how to write the theoretical part of the project, to perform the necessary technical calculations for the project, to perform sketch design.

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

Module aim

Course aim – to provide fundamental theoretical and practical knowledge about the Internet of Things systems and software of such systems, operating algorithms and principles of administration.

Module description

Module introduces the Internet of Things system structure, working algorithms of the separated components and software development aspects. Analyses such things as dynamic structure of IoT devices, principles of IoT portals, databases, security of IoT systems.
Students are required to attend all theoretical lectures, with attendance exceeding 50% over the course of the semester. Students must complete all assigned laboratory work, with at least 80% of these tasks completed as scheduled.

Module aim

To provide knowledge’s about cloud computing.

Module description

Introduction to cloud computing. Cloud computing – architecture, virtualization, service management, security.
Students must attend at least 80% of the time scheduled laboratory work.

Module aim

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.

Module description

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.