Artificial Intelligence Systems

Module aim

The aim of the course is to introduce students to the data structures and their practical realization by creating the corresponding functions and classes, teaching algorithms, analyzing and evaluating them.

Module description

The subject provides a systematic analysis of data structures, including massive linear lists, linear and hierarchical data structures. Familiarized with the list, stack, deck, heap, binary tree, Bayer tree, red-black tree. The procedures with elements of the data structure are explained, the memory demand is analyzed, the restrictions and efficiency of data structures are discussed.
Algorithms ar presented, their properties are analyzed, algorithm complexity and asymptotic analysis of algorithms are presened. Comprehensive analysis of fast and slow sorting algorithms is presented. Searching and recursive algorithms are explained, dynamic programming is introduced. The principles of data exchange between external and internal memory in sorting and searching algoritms are analyzed when very large data are used.
Students must attend at least 80% of the time scheduled laboratory work. Mandatory minimum attendance of module lectures – 50%.

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 80% of the time scheduled laboratory work and at least half of the lectures at the scheduled times.

Module aim

Provide knowledge about mechatronic equipment and systems; match theory and practice elements, interpret experimental data, choose and apply mathematical methods for simulation of mechatronic equipment and systems, acquire ability to use advanced informational technologies for preparing graphical and text documentation of investigation into mechatronic systems.

Module description

The mechatronic system definition and the main elements are considered transformers, the principle of their operation, equivalent circuits, phasor diagrams, characteristics; construction of direct current machines, principle of their operation and control methods; induction motors, the principle of operation and control methods; small power synchronous motors, their characteristics, control methods; stepper motors and their control; sensors of mechatronic systems: tachogenerators, resolvers, encoders of rotational speed and position.
Students must complete at least 80% of the laboratory work during the scheduled time;
Students must complete all scheduled laboratory work.
at least half of the lectures at the scheduled times

Module aim

Learning to program mathematical scripts and functions, internet pages and scripts to master modern script programming technologies and be able to apply them to solve engineering problems.

Module description

Script programming subject delivers knowledge about programming of mathematical functions and scripts, 2D and 3D graphics, Web pages, their style and control scripts. Programming with Matlab, HTML-kit and ATOM software and qualified application of it to solve engineering problems is taught. Abilities to combine theoretical and practical elements, to experiment, analyze and interpret data are exercised. Abilities to work independently and responsibly, thoroughly schedule own work and time are developed.
Students must complete all scheduled laboratory work. Students must attend at least 80% of the course laboratory and at least half the lectures according to the semester Lecture schedule.

Module aim

Provide a basic knowledge of stochastic nature of events and their mathematical models and the ability to apply this knowledge practically.

Module description

The module of study subject introduces the main concepts and definitions of Probability Theory – a random event, its probability, sum and multiplication of probabilities theorem, total-probability and Bayes formulas, the Normal and Poisson approximations to the Binomial probability. Random variables and their distribution analysis, independence of random variables. The most common distribution laws of random variables (vectors), the analysis of numeric characteristics of random variables and their dependence are introduced. The empirical analogues of the theoretical distribution characteristics and the basic concepts of mathematical statistics are defined.

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

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 the course is to introduce the theoretical and practical aspects, issues and applications of public communication.

Module description

Public Communication course aims to introduce personal branding, corporate communication, communication with clients and internal communication, the students who have chosen studies of engineering sciences, computer sciences, technology sciences, mathematics sciences. Students learn how to present themselves and their ideas, better speak in public, to make good and convincing points, to better use the internet and social media for their professional goals, also to understand cross-cultural communication. The importance of media channels, messages and communication to target audiences are also introduced in the course. Through practical tasks for personal branding, students will learn how to adopt public ethics, protocol standards. In this course an approach of learning by doing is combined with theoretical analysis and students’ self-reflection. The practical part of the course consists of active participation in discussions during different exercises, case studies as well as preparation and presentation of public speeches and presentations.
Participation in at least 60% of the scheduled exercises is mandatory. Lecture attendance is at least 50%

Module aim

To gain the fundamental knowladge of machine learning, which allow independently to create a machine learning model, perform model testing, and to evaluate obtained results.

Module description

Nowadays machine learning is one of the most commonly analyzed areas in the scientific literature. Uncomplicated machine learning models are applied in different types of systems with capabilities to analyze different types of data (numerical, textual, images). During this course, students will learn the main stages of developing a machine learning model: data extraction, preparation, clasifier realization, testing, and evaluation. The course will also review the difference between classification and clustering, the commonly used algorithms will be explained. The main focus of this course is to learn how to create machine learning model from scratch, to perform the model testing, and to be able to evaluate and analyze the obtained results.
Students must attend at least 80% of the time scheduled laboratory work. Mandatory minimum attendance of module lectures – 50%.

Module aim

The primary aim of the module is to familiarize students with the basic concepts, methods and tools of software systems engineering. Train UML modelling skills.

Module description

The module introduces the concept of software engineering. Students are familiarized with the process of software systems development and the main concepts of software systems engineering and requirements engineering. Students are familiarized with possible software systems in organizations and their types. Another topic covered is the software crisis and why it arises. The following topics are covered during the lectures also: main software development principles and paradigms, software systems analysis and conceptual modelling, including modelling with UML. Students are introduced to the main problems, techniques, strategies, and templates of software systems design. The following topics are also covered: software systems testing and quality assurance. The module also teaches how to write technical reports and other technical documents.
Students must attend at least 80% of the time scheduled laboratory work. Mandatory minimum attendance of module lectures – 50%.

Module aim

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.

Module description

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.

Module aim

To provide students with basic knowledge in economics, formulating systemic understanding of market economics relations, tendencies as well as practical skills, relevant for making and implementing economic decisions in their professional activities.

Module description

During the couse of Economics is studied the theory of economics, the object, problems and goals of economics. The main topics of economics studies include: competition models and mechanism, conception of national product and calculation methods, fiscal and monetary policy, their aims and operation means, conception of inflation, kinds of inflation, evaluation of inflation, unemployment and employment policy, international economics and international economic links.
Students must attend at least 60 % of the time scheduled exercises.
Minimum mandatory attendace of module lectures is 50%.

Module aim

Providing knowledge of matematics and physics and ability to apply knowledge in design and optimization of electronic devices. Providing knowledge of modern electronic devices and their applications in various fields of science and technology. Preparation for further studies of electronic circuits and other subjects.

Module description

Introduction. Semiconductor diodes. Bipolar transistors. Field effect transistors. Thyristor devices. Semiconductor integrated circuits technology. Bulk and surface acoustic wave devices. Optoelectronic devices. Display devices. Summary. Attendance at theoretical lectures is mandatory, and to qualify for sitting the exam in the first take, students must have recorded attendance of at least 50% of the lectures. Students are required to attend theoretical lectures – more than 50% of them must be attended during the semester. Students must complete all scheduled exercises. Students must attend at least 80% of exercises during the scheduled time.

Module aim

To prepare project managers, practice on project management tools and methods.

Module description

Introduction to Project Management. Project Management Basics. Initiating Processes. Planning Processes. Project Scope Planning. Project Time Planning. Project Cost Planning. Project Quality Planning. Project Human Resources Planning. Project Communication Planning. Project Risk Planning. Project Procurement Planning. Project Executing Processes. Project Monitoring and Controlling Processes. Closing Processes.
Students must attend at least 80% of the time scheduled laboratory work and at least half of the lectures at the scheduled times.

Module aim

To enable students to form a theoretical management knowledge base and to develop practical abilities by analyzing the management processes in electronic engineering organization and choosing the effective ways of solving problems of the organization.

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 are analysed 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 analysed 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 of principles, its process, pay for work and motivation. There are disputed change and conflict management. Students must attend at least 60% of exercises and at least half of the theoretical lectures according to the timetable.

Module aim

Learning operating system concepts to cognize operating system functioning, algorithms and data structures implemented in operating systems, to be able to manage real operating systems.

Module description

Operating system concepts subject delivers knowledge about operating system purposes and working, process and thread concepts, CPU scheduling, critical section concept and process synchronisation, deadlock definition and management methods, main memory management technologies and virtual memory organization, file system. A modelling and investigation structure of operating systems in the application software environment of the applied software is taught.
Abilities to combine theoretical and practical elements, to experiment, analyse and interpret data are exercised. Abilities to work independently and responsibly, thoroughly schedule own work and time are developed. 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 Lecture schedule.

Module aim

Teach to understand the processes which occur during the propagation of signals in linear and nonlinear circuits and develop the ability to evaluate changes which occur in signals.

Module description

Classification of signals, deterministic broadband spectra of periodic and non-periodic signals and their properties, Laplace transformation and its properties. Spectra of narrowband signals. Analysis of the signal changes in linear circuits by using different methods: classic, operators and time domain methods. Applications of nonlinear devices in order to change the frequency of signal spectrum: multiplication and replacement, modulation and detection. Students must complete all scheduled laboratory work.
Students must ettend at least 80% of the course laboratory and at least half of the lectures according to the semester Lecture schedule.

Module aim

Provide sufficient knowledge of design and analysis of digital devices and develop the ability to apply the acquired knowledge in engineering activities. Develop the need to be interested in electronics and electrical engineering. Develop the ability to maintain their professional competence by life-long learning.

Module description

Digital devices subject delivers knowledge about number systems and codes, digital logic functions, logic algebra, combinational logic design, combinational and sequential logic circuits, bistable memory devices, synchronous sequential logic circuit design.
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

The aim of the subject is to acquire knowledge on the computer network fundamentals, acquire the abilities to describe structure, architecture and operation of computer networks, to investigate topology and function of a network, to plan and develop a computer network, carry out the tasks of network administration.

Module description

Subject focuses on common OSI model, principles of data exchange (communication medium, data coding, data transmission interface, control of the data transmission channel, data compression). The protocols of the computer network are reviewed (the protocols of interaction between computer networks, transport protocols). The term LAN is introduced, the LAN technology, LAN systems are analysed in this subject. The term WAN is introduced, the channel switching, packet switching technologies, ATM and Frame Relay protocols are analysed. The subject also focuses on the security of the computer network, distributed computer network systems, tools for computer network administration.
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 give a comprehensive introduction to computational methods for understanding and generating human language and text by combining classical algorithms and contemporary machine learning.

Module description

This course provides a technical perspective on text and natural language processing-methods for building computer software that understands and generates human language and text. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The course first establishes a foundation in machine learning by building a set of tools that will be applied to word-based textual analysis. The course then introduces structured representations of language, including sequences, trees, and graphs. This is followed by an exploration of different approaches to the representation and analysis of linguistic meaning. Finally, an introduction is given to three applications of natural language processing: information extraction, machine translation, and text generation. 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

This course is designed to provide an in-depth introduction to deep learning. It is designed for students who want to understand and apply deep learning methods to solve a variety of problems. The course provides students with both a solid theoretical foundation for deep learning and in-depth practical application knowledge. The aim of this course is to provide students with a solid foundation for recognizing which problems can be solved through deep learning by designing and teaching various neural network models.

Module description

The Deep Learning course is designed to provide the knowledge needed to solve the challenges in artificial intelligence using deep neural networks. This course teaches the basics of deep learning, teaches how to build neural networks, and provides the skills to lead successful machine learning projects. During this course, students will learn how to simply but accurately describe what deep learning is; explain how deep learning can be used to develop different models of information systems; identify practical issues that may benefit from deep learning. The course will teach the applications of deep learning to solve data analysis classification and clustering problems related to images, text, sound, time series, and business systems data.
Upon completion of the course, students will know how to initialize and apply such deep neural network architectures as convolutional networks, RNN, LSTM, Adam, Dropout, BatchNorm, Xavier. The theoretical knowledge acquired during the course is reinforced by practical classes with the use of Python, Keras and TensorFlow software packages. Students will be taught to run models using both GPU and CPU computing resources, and students will be taught to reuse pre-prepared models to reduce training time and cost (i.e. transfer learning).
Mandatory minimum attendance of module lectures – 50%.

Module aim

Learning operating system concepts to cognize operating system functioning, algorithms and data structures implemented in operating systems, to be able to manage real operating systems.

Module description

Operating system concepts subject delivers knowledge about operating system purposes and working, process and thread concepts, CPU scheduling, critical section concept and process synchronisation, deadlock definition and management methods, main memory management technologies and virtual memory organization, file system. A modelling and investigation structure of operating systems in the application software environment of the applied software is taught.
Abilities to combine theoretical and practical elements, to experiment, analyse and interpret data are exercised. Abilities to work independently and responsibly, thoroughly schedule own work and time are developed. 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 Lecture schedule.

Module aim

The aim of this course is to introduce models, principals and methods of regression analysis and related model, their applications and interprataion in econometric studies and to teach students to perform with computers statistical analysis of real economic data.

Module description

The basis of this introductory econmometric course is regression analysis and related models: dispersion and factor analysis. The aim of the course is to introduce principals of econometric analysis, main concepts and methods of regresion, dispersion and factor analysis, to teach students to apply this methods in real studies and correctly to interpret the results.
Students must attend at least 80 % of the time scheduled practical lectures and at least 80 % laboratory works.

Module aim

The aim of this course is to introduce students to natural language processing systems. The aim of the course is to present both detailed theoretical methods of natural language processing and practical implementation of those methods. The course examines a wide range of tasks related to natural language processing. Both classical text analysis algorithms and modern deep learning methods are analyzed.

Module description

The course covers most of the tasks in natural language processing. The course begins with a discussion of classical methods in natural language processing. Students are introduced to the morphological, syntactic, semantic and pragmatic processing of languages. After completing the course, students will be able to recognize NLP tasks in their daily work, offer methods to solve the set practical tasks. All lectures aim to find a balance between traditional and deep learning methods in the field of NLP. For example, after discussing the statistical and grammatical models fot machine translation, we later show how these models combine with encoding and decoding systems in deep neural networks.
The course introduces the basics of linear algebra and probability theory in natural language problems, provides basic knowledge of the use of deep neural networks in solving natural language data coding problems. During the course, the methods discussed in detail are related to today’s practical tasks: Internet search, advertising presentation, e-mail analysis, customer service using language robots, foreign language translation, creation of virtual agents, automatic generation of various reports and documents. Much attention has been paid in recent years to deep-learning (or neural network) approaches to many different NLP tasks, using models that do not require traditional, task-specific functions. In this course, students will receive a thorough introduction to cutting-edge NLP deep learning research. During the lectures, assignments, and final project, students will learn the skills necessary to create, implement, and understand their natural language models.
Mandatory minimum attendance of module lectures – 50%.

Module aim

The aim of the subject is to acquire knowledge on the computer network fundamentals, acquire the abilities to describe structure, architecture and operation of computer networks, to investigate topology and function of a network, to plan and develop a computer network, carry out the tasks of network administration.

Module description

Subject focuses on common OSI model, principles of data exchange (communication medium, data coding, data transmission interface, control of the data transmission channel, data compression). The protocols of the computer network are reviewed (the protocols of interaction between computer networks, transport protocols). The term LAN is introduced, the LAN technology, LAN systems are analysed in this subject. The term WAN is introduced, the channel switching, packet switching technologies, ATM and Frame Relay protocols are analysed. The subject also focuses on the security of the computer network, distributed computer network systems, tools for computer network administration.
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

Provide students with practical knowledge of IoT devices, their design and programming capabilities, networking, data transmission and local or centralized data processing technologies.

Module description

Design of internet of things devices course project delivers: knowledge about design and programming of IoT devices; the skills of connecting IoT devices to a common network and the knowledge how to process the stored data locally in the embedded system by using edge computing or in servers by using cloud computing. Students must attend at least 60% of the practical exercises (practical work) according to the semester Lecture schedule.

Module aim

Provide students with practical knowledge of the exchange of data, data processing, analysis technologies and their practical application in online systems.

Module description

During the coursework project, the data transfer solutions and programming of items connected to a common network are analysed. The fundamentals of the data processing in embedded systems are analysed and the application of data processing algorithms are implemented. Students learn how to formulate a problem based task, how to split it into multiple tasks, and how to prepare a plan for implementation of these tasks. During the project students get familiar with dedicated software tools and their application to solve a specific practical task. Students must complete all scheduled laboratory work.
Students must attend at least 60% of the practical exercises (practical work) according to the semester Lecture schedule.

Module aim

To get familiar with the core concepts of cloud computing, architecture, services and the basic principles of their application in the modern IT solutions.

Module description

The study subject provides an introduction to cloud computing technologies, explaining its history, emerging trends, and the business case for cloud computing. The subject analyzes the various cloud service models (IaaS, PaaS, SaaS) and deployment models (Public Cloud, Private Cloud, Hybrid Cloud) and the key components of a cloud architecture (Virtualization, VMs, Storage, Networking). Theoretically and during practical classes, the principles of containers technologies and their orchestration are analyzed. Examples from different applications are provided: data protection, backup and restore, disater recovery and operation continuity, high availability solutions, cost models of differend cloud service providers. During the practical work of the subject, modern open source virtualization software (KVM, XCP-NG) and container technologies (Docker/Kubernetes) are applied.
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

Dalyko tikslas yra suteikti pagrindines žinias apie didžiuosius duomenis (Big Data) bei jų apdorojimo technologijas.

Module description

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 80% of the time scheduled laboratory works.

Module aim

To provide students with sufficient knowledge of microcontroller architecture and programming and to develop the ability to apply that knowledge in engineering activities. To develop the need to seek for the knowledge of programming of embedded systems. To develop the ability to maintain their professional competence by life-long learning.

Module description

The study module of microcontroller systems provides knowledge about 8 and 32 bit microcontrollers. The architecture of microcontrollers is discussed in details: kernel, registers, memory types, general purpose and specific pins, interrupts, timers-counters, analog to digital converters. The communication interfaces are also presented in details. The programming of peripheral devices such as sensors, displays, external memories, is discussed. 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

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

To provide students with informatics engineering knowledge about the infrastructure of modern e business systems, data processing workflows, and the principles of developing online stores. The course aims to develop the ability to critically evaluate e business technologies, apply recommendation and forecasting systems, optimize e business processes, and design effective customer acquisition strategies. Students are encouraged to creatively use artificial intelligence technologies when developing e business solutions and to continuously improve their professional competencies in a rapidly evolving digital environment through lifelong learning.

Module description

In the Electronic Business Systems course, students become familiar with modern practices of developing, managing, and analyzing e business. Starting with the concept and infrastructure of electronic business, the course examines principles of data collection, processing, and technical system operations. Students gain practical experience in creating and managing an online store, applying recommendation systems, and using search engine optimization methods to increase online visibility.
The course emphasizes customer acquisition strategies, analysis of advertising channels, and interpretation of consumer behavior data. It also explores the application of forecasting algorithms in electronic business systems to anticipate customer needs, identify sales trends, and optimize organizational performance.
Students are required to complete all assigned practical and laboratory work. Participation must include at least 60% of tutorials, at least 80% of laboratory sessions, and at least half of the scheduled theoretical lectures.

Module aim

To get familiar with the core concepts of cloud computing, architecture, services and the basic principles of their application in the modern IT solutions.

Module description

The study subject provides an introduction to cloud computing technologies, explaining its history, emerging trends, and the business case for cloud computing. The subject analyzes the various cloud service models (IaaS, PaaS, SaaS) and deployment models (Public Cloud, Private Cloud, Hybrid Cloud) and the key components of a cloud architecture (Virtualization, VMs, Storage, Networking). Theoretically and during practical classes, the principles of containers technologies and their orchestration are analyzed. Examples from different applications are provided: data protection, backup and restore, disater recovery and operation continuity, high availability solutions, cost models of differend cloud service providers. During the practical work of the subject, modern open source virtualization software (KVM, XCP-NG) and container technologies (Docker/Kubernetes) are applied.
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

Dalyko tikslas yra suteikti pagrindines žinias apie didžiuosius duomenis (Big Data) bei jų apdorojimo technologijas.

Module description

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 80% of the time scheduled laboratory works.

Module aim

Module aims to provide knowledge and skills to work with modern internet and web technologies.

Module description

Module provides knowledge about internet technologies such as XML, XSLT, HTML, CSS, JavaScript, AJAX, backend technologies and frameworks. The knowledge about newest internet systems modelling, programming and installation is provided. A lot of attention is paid for practical examples.
Mandatory minimum attendance of module lectures – 50%.

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

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.

Module description

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.

Module aim

Aim is to familiarize with the companies using and (or) developing intelligent electronic systems, their structure and business activities, analyse the developed systems, acquire the new knowledge, cognitions, special and general abilities on the intelligent electronic systems application.

Module description

The subject is focused on the familiarization with the companies developing, applying or using intelligent electronic systems. Structure analysis of the company or its department, analysis of the main department functions, activities, industrial relations and products. Students perform a detail analysis of the intelligent electronic systems and innovative systems technologies that are used or developed in the company or its department. Also the student should perform an individual practical work by performing a problem based task given by the head of the company or department. The results of the practice are presented by preparing an individual report on practice results and public presentation with generalization of the results and final conclusions.

Module aim

Aim is to acquire knowledge about development and improvement of the modern DSP tools, acquire cognitions about their operating principles and application possibilities and abilities to choose a reasoned solution, working individually or in group.

Module description

Digital signal processing tools course aim is to acquire knowledge about modern means of digital signal processing, their operating principles and application possibilities. In this course there are analysed: Finite Impulse Response digital filters, Infinite Impulse Response digital filters, filter structures. For non-linear digital signal processing are analysed Artificial Neural Networks: Single-Layer Perceptron, Multi-Layer Perceptron, Self-Organizing Maps, applications and learning algorithms of the neural networks. Knowledge about digital signal processing tools, its design, modelling and application for audio and image signal processing is acquired. Problems and solutions of digital speech signal processing, modelling and synthesis are analysed. Modern digital signal processors and specialized programming tools are analysed. Students must complete all scheduled laboratory work. Students must complete at least 80% of the course laboratory according to the semester Lecture schedule.

Module aim

Becominc familiar with AI system algorithm programming basics and technical equipment programming peculiarities.

Module description

The hardware programming module focuses on the basics of the programming of embedded systems, the specificities of hardware programming. The structure of the selected computerised hardware shall be analysed, with the devices and sensors available to it. Practical skills for scanning sensor data and managing devices. Algorithms adapted to technical equipment and their implementation shall be analysed. Students must complete all scheduled laboratory work. Students must complete at least 80% of the course laboratory and at least half of the lectures according to the semester schedule.

Module aim

To prepare, coordinate with supervisor and start to carry out according to the work schedule group (individual) Bachelor Final Thesis problem-oriented task in the field of study specialization, to deepen knowledge and cognition about newest specialized electronic systems while preparing literature review on analogues, to acquire for these task necessary special and general skills.

Module description

Final Thesis 1 subject delivers knowledge about newest electronic systems, their characteristics, structure and design, artificial intelligence electronic systems, their characteristics, structure, design and analysis. Abilities to combine theoretical and practical elements, to apply information technologies, to assess and analyze literature and data, to plan work and have holistic attitude are exercised. Preparation of group (individual) Bachelor Final Thesis problem-oriented task, work schedule, analysis of literature on analogues, are learned. Abilities to thoroughly prepare graphical and textual documentation, to responsibly schedule own work and time, to be communicative working in a team and making oral presentation, are developed. Students must attend at least 60% of the practical exercises (practical work) according to the semester Lecture schedule.

Module aim

Access to computerized information systems and designing (or) consuming enterprises, their structure and activities, analyze them designed and implemented systems to gain new knowledge and understanding of the special and general skills in the design of the latest computerized information systems.

Module description

Study subject meant access to computerized information systems design, are implementing or applying their ventures. Access to the company or its subsidiary structure, department functions, communication and production. Practice during the familiarization with the law department facilities and activities. Students are fully engaged in the enterprise or its department designed and installed computerized information systems and progressive technology analysis. It also carried out the practice guide the company appointed an individual problematic task. Practice results are presented by developing individual practice report and a public report, which summarizes the results and formulation of conclusions.

Module aim

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

Module description

“Programming of Internet of Things” module introduces the Internet of Things system structure, working algorithms of the separated components and software development aspects. Analyses such things as dynamic HTTP pages, self-organizing sensor networks, databases.
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

The module presents a framework for understanding the role of AI in information systems, problem solving, knowledge
representation, reasoning, knowledge-based systems and knowledge managemen.

Module description

The module is devoted to a deeper analysis of the concepts of artificial intelligence and the application of artificial intelligence in information systems. The structure of the module includes the following topics of artificial intelligence: the concept of artificial intelligence, the history of artificial intelligence development, the role of artificial intelligence theory in information systems, search in state space (standard search, heuristic search, planning, operation and learning, modern search methods), knowledge representation and reasoning. Students are introduced to the theoretical aspects of knowledge acquisition, practical methods of knowledge acquisition and their classification, expert systems, the use of knowledge-based systems in information systems, problem solving using artificial intelligence. The course focuses on the use of Markov decision processes in artificial intelligence systems. Students are introduced to Bayesian network methods, their use in various information systems. The course concludes with an examination of specific information systems: autonomous cars, artificial intelligence assistants, automatic game robots, image processing systems.
Mandatory minimum attendance of module lectures – 50%.

Module aim

To prepare, coordinate with supervisor and start to carry out according to the work schedule group (individual) Bachelor Final Thesis problem-oriented task in the field of study specialization, to deepen knowledge and cognition about newest artificial intelligence solutions while preparing literature review on analogues, to acquire for these task necessary special and general skills.

Module description

Final Thesis 1 subject delivers knowledge about newest artificial intelligence solutions. Abilities to combine theoretical and practical elements, to apply information technologies, to assess and analyze literature and data, to plan work and have holistic attitude are exercised. Preparation of group (individual) Bachelor Final Thesis problem-oriented task, work schedule, analysis of literature on analogues, are learned. Abilities to thoroughly prepare graphical and textual documentation, to responsibly schedule own work and time, to be communicative working in a team and making oral presentation, are developed.

Module aim

To increase the specific knowledge and understanding of the latest, artificial intelligence specialised electronic systems development through the implementation of intelligent electronic system, acquire the special and key competences necessary for this work

Module description

Final Thesis 2 subject delivers knowledge about the implementation of electronic AI based systems in hardware and software, development of electronic embedded systems and embedded system optimized program algorithms for AI based electronic systems. Abilities to combine theoretical and practical elements, to apply information technologies, to assess and analyze data, to plan work and have holistic attitude are exercised. Implementation of AI based electronic system is learned. Abilities to thoroughly prepare graphical and textual documentation, to responsibly schedule own work and time, to be communicative working in a team and making oral presentation, are developed. Students must attend at least 60% of the practical exercises (practical work) according to the semester Lecture schedule.

Module aim

To deepen knowledge and cognition about newest specialized artificial intelligence electronic systems while screening electronic system, to acquire for this task necessary special and general skills and by the Bachelor Final Thesis to prove that competence and abilities acquired during studies confirm requirements of qualification for Bachelor’s degree in Informatics Engineering.

Module description

Final Thesis 3 subject delivers knowledge about screening of electronic systems, scheduling of the experiments and test of prototypes of artificial intelligence electronic systems. Abilities to combine theoretical and practical elements, to apply information technologies, to assess and analyze data, to plan work and have holistic attitude are exercised. Screening of artificial intelligence electronic system is learned. Abilities to thoroughly prepare graphical and textual documentation, to responsibly schedule own work and time, to be communicative working in a team and making oral presentation during the defense, are developed.

Module aim

The aim of the subject is to apply the knowledge and skills acquired in studied subjects to develop a Artificial intelligence system accordingly to the set parameters and application destination, learn to design the structure of the system, analyze it and plan the development timing.

Module description

The complex course project for the design of AI systems aims to combine knowledge and skills acquired in the study subjects “Machine Learning”, “Text and Natural Language Processing”, “Software Systems Engineering”, “Digital Signal Processing Tools”, “Image Recognition Systems”, “Hardware Programming”, “Microcontroller Systems” to design an artificial intelligence system operating in the embedded system. The cross-cutting coursework project takes into account the application of knowledge and skills in practice, the development of AI systems, the reasoned selection of methods and software tools for solving the problems and challenges raised and the software tools. During the preparation of the complex coursework project, students are focused on the second and third stages of final thesis preparation. Students must attend at least 60% of the practical exercises (practical work) according to the semester Lecture schedule.

Module aim

To provide knowledge on information security methods and principles.

Module description

This module presents basic information security insurance aspects. Basic concepts and models are being described and analyzed; major information security insurance technologies are presented and described. Primary attention is dedicated to technological information security aspects, as well as legal and managemental aspects of information security insurance.
Mandatory minimum attendance of module lectures – 50%.

Module aim

The aim of this subject is to acquire knowledge about the fundamentals of the multimedia systems, its design, analysis and applications. Educate the cognitions to combine theory and practice to develop and analyse innovative multimedia systems.

Module description

In this subject the innovative multimedia systems are presented. The subject begins with discussion on the multimedia systems evolution, short review of the innovative multimedia devices and new standards. The human hearing systems is reviewed, its relations with innovative audio processing, storage, transmission and restoration. Innovative digital audio and video technologies are presented and new tendencies of the audio technology are discussed. 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 Lecture schedule.

Module aim

To introduce a student with the peculiarities of the scientific style, the requirements for the terms, the principles of terms regulation, the regularities of Professional language, To teach to write and edit a scientific text.

Module description

The academic style and its place in the system of functional styles is analyzed. The differences of spoken and written language, the public and non-public language features are discussed. A detailed analysis of the terms concept, types, requirements, structure, terminology management techniques is presented. The focus on the analysis of scientific language expression patterns and disadvantages of scientific text composition features. Participation in at least 60% of the scheduled exercises is mandatory.

Module aim

To indoctrinate students with the fundamentals of law, with system of law and order, the basic legal acts.

Module description

In the law course, non-legal specialty students are introduced to the main aspects of the Lithuanian legal system in an attractive way, the sources of law are examined, legal relationships are revealed, and legal responsibility is assessed. There is a strong focus on legality, law and order, discussing legal behavior and the validity of legislation.

Module aim

To prepare final work.

Module description

Problems’ formulation and solving using artificial intelligence solutions.

Module aim

To present Final Work

Module description

Project and prepare final thesis.

Module aim

The aim of this module is to apply the knowledge and skills acquired during the studies for the development of artificial intelligence systems and to provide experience in self-planning and implementation of artificial intelligence systems development projects.

Module description

Students will apply the basics of artificial intelligence, machine and deep learning, text and natural language recognition, image recognition, big data processing, decision making and project management skills for the development of artificial intelligence systems. During the semester each stage of artificial intelligence system development is analyzed, starting with bussiness requirements, goal formulation. After that proper data selection, data processing, selection of appropriate artificial intelligence algorithms, model development, testing and implementation in production is done. Finally, the success, results and possibilities of applying the developed system in other areas are evaluated. The course project prepares for the completion of the second and third stages of final thesis and defence of the thesis.

Module aim

To provide knowledge on information security methods and principles.

Module description

This module presents basic information security insurance aspects. Basic concepts and models are being described and analyzed; major information security insurance technologies are presented and described. Primary attention is dedicated to technological information security aspects, as well as legal and managemental aspects of information security insurance.
Mandatory minimum attendance of module lectures – 50%.

Module aim

To provide knowledge about the principles and techniques of developing intelligent assistance systems and to teach to create a real functioning intelligent assistance system.

Module description

Intelligent Assistance Systems are new, rapidly evolving technologies designed to help people make decisions, meet cognitive needs, or assist in the event of a physical disability. The study subject introduces the purposes, architecture, development and implementation of these systems. The subject examines the applications of natural language processing in the management of personal assistants, the architecture of multi-agent systems, provides knowledge about the development methods, technologies, communications, programming interfaces of such systems.
Mandatory minimum attendance of module lectures – 50%.

Module aim

To introduce a student with the peculiarities of the scientific style, the requirements for the terms, the principles of terms regulation, the regularities of Professional language, To teach to write and edit a scientific text.

Module description

The academic style and its place in the system of functional styles is analyzed. The differences of spoken and written language, the public and non-public language features are discussed. A detailed analysis of the terms concept, types, requirements, structure, terminology management techniques is presented. The focus on the analysis of scientific language expression patterns and disadvantages of scientific text composition features. Participation in at least 60% of the scheduled exercises is mandatory.

Module aim

To indoctrinate students with the fundamentals of law, with system of law and order, the basic legal acts.

Module description

In the law course, non-legal specialty students are introduced to the main aspects of the Lithuanian legal system in an attractive way, the sources of law are examined, legal relationships are revealed, and legal responsibility is assessed. There is a strong focus on legality, law and order, discussing legal behavior and the validity of legislation.