Information Electronics Systems
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DepartmentFaculty of Electronics
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Program code6211BX015
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Field of studyComputer Sciences
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QualificationMaster of Informatics Sciences
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Duration2
About
INFORMATION ELECTRONICS SYSTEMS
| Degree | Master of Informatics Sciences |
| Length | 2 years (4 semesters) |
| Study language | Lithuanian |
| Start | 1st of September |
| Entry Qualification | To this programme applicants are accepted from the fields of: Electronics Engineering, Electrical Engineering, Information Systems, Software Engineering, Informatics Engineering. |
Getting the answer to the desired question when using only electronic equipment for talking. Getting a medical diagnosis by merely showing a sore spot. Getting personalized experience of a beneficial business offer while living at a normal pace. Getting program source codes and free help from like-minded people while developing electronic and information systems. These are only a few applications of intelligent or open systems based on the progress of information technologies that are already in use, when human involvement in service provision is no longer required.
A modern information electronics system is a combination of hardware and software. It is complex both in terms of structural and applied knowledge. For this reason, there will always be a great need for professionals capable of developing, applying, maintaining or otherwise using it.
Master's students in Information Electronics Systems are given in-depth knowledge of artificial intelligence and open source electronic systems and services. The students also develop the capacity to develop and apply them. Furthermore, Master‘s students are challenged to analyze how to best conduct applied research in the field of computer engineering.
This Master's study program offers the following two specializations – Artificial Intelligence Systems and Open Source Systems.
Artificial intelligence systems can independently perform tasks that were previously performed exclusively by human beings. Among such tasks are image perception, speech recognition, identification of a person and his/her behavior, or even real-time decision making.
Modern Internet of Things or Services Technologies are inseparable from smart data merging, automated analytics, decision rule generation, or long-term forecasting. All this has become possible due to advancement in artificial neural networks and machine learning technologies.
Technical progress in computer hardware, in particular multithreaded processors and graphics accelerators, as well as the supply of open source software, has laid the foundations and created opportunities for the application of artificial intelligence in almost all areas of life and creativity. Many of us have heard of such systems as Google AI, Tensor Flow, Microsoft Azure, Amazon Lex or IBM Watson, to name but a few examples of the information technology market.
Students in this specialization will gain knowledge on how to independently design, operate, and further develop embedded systems of information processing, diagnostics and management based on artificial intelligence, to be applied in the field of biomedicine.
Open source systems bring communities together, contribute to finding new solutions that influence the development of not only these systems but also those of proprietary applications. Open source software applications are being developed and increasingly used around the world. MIT, Apache, BSD, GNU and other licenses made it possible to develop plenty of tailor-made software for various activities and areas, making them an integral part of everyday life.
Such application software includes the popular web browser Mozilla Firefox, office suite LibreOffice or Android and Linux operating systems, all of which are well known to many users. For more advanced users, the following dedicated software has been developed: the Ethereum block chain, block chain programming languages Python and PhP, integrated application development platforms of the Internet of Things such as ThingsBoard, ThingSpeak and Arduino, or even the artificial intelligence development platforms TensorFlow, IBM Watson and Apache Mahout.
Students in this specialization will learn how to design, operate, and further develop distributed open source information systems.
What competencies will I acquire?
The Master's study program in Information Electronics Systems is designed to acquire the following skills:
- to independently develop, operate and further develop embedded systems of information processing, diagnostics and management based on artificial intelligence, to be applied in the field of biomedicine;
- to select and apply mathematical methods, software and hardware aimed at solving engineering problems, data analysis and interpretation;
- to carry out specialized research activities and make innovative decisions based on research results.
What are my possible career pathways?
- Having an information technology project manager‘s job, working as an electronic and information system analyst or a specialist in the development and maintenance of these systems.
- Having an information technology project manager‘s job, working as an electronic and information system analyst or a specialist in the development and maintenance of these systems.
- Teaching at universities and colleges.
- Pursuing doctoral studies in the chosen field of technology.
Study subjects
1 Semester
obligatory
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ELESM17108 9 credits
Signals and Signal Processing
Module aim
Increasing of knowledge and skills in theoretical analysis of signals and their processing methods in newest technology. Be able to explain the proposed solution, self-employed or diverse.
Module description
The course of Signals and Processing provides knowledge on the basic items of signal theory: the analysis techniques of signals changing in electronic circuits, dynamic signals mapping, geometrical methods of signal theory, orthogonal signals theory, methods of calculating the amount of information. The mathematical models of fixed range signals and their associated sampling theorem, analytical signal, Gilbert’s transformation are analyzed. During the study of discrete signals and their processing the students learn to make mathematical models of discrete signals and calculate the signal characteristics. The skills to analyze creation, processing and utilization of digital signals are acquired. Analog-digital and digital-analog converters, the fast Fourier transform and its applications, digital filtering algorithms in time and frequency domain, the digital device speed are analyzed. Students must complete all scheduled laboratory work.
Students must attend at least 60% of the practical exercises (practical work) and at least half of the lectures according to the semester schedule. -
ELESM17107 6 credits
Intelligent Systems
Module aim
To introduce for students the mathematical methods used in modern intelligent systems, the elementary elements that make up these models, and to help students acquire practical skills in choosing the most suitable solution for the selected task, arguing the appropriateness of the selected solution, distinguishing advantages and formulating a technical task for the implementation of the solution.
Module description
Knowledge is gained about intelligent systems based on artificial neural networks, evolutionary calculations or fuzzy logic, their composition and principles of operation. New concepts for the application of intelligent systems are analyzed, the choice of methods, efficiency measurements and comparative studies are critically evaluated. It is learned to independently create individual components of intelligent systems, to model intelligent systems or their parts with MATLAB software or in the Python environment, and to apply them to analyze and process sound, image and other signals of a technical nature. 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. -
ELKRM17104 6 credits
Mathematical Modelling Technologies
Module aim
To learn design and develop mathematical models of electronic circuits and systems using modern modeling technologies, critically analyze modeling results and draw conclusions.
Module description
Mathematical modelling technologies subject delivers knowledge about mathematical modelling, numerical methods, application of linear and nonlinear equations, matrices and differential equations for description and numerical modelling of electronic circuits and systems, numerical integration and differentiation, data processing, analysis and visualization.
Students must complete no less than 80% of the scheduled laboratory works -
ELESM17109 6 credits
Fundamentals of Research and Innovations
Module aim
To deliver knowledge about research, development and innovation systems, to develop scientific work planning, performing and organizing skills and scientific results public presentation abilities.
Module description
Fundamentals of Research and Innovations subject delivers knowledge about research, development and innovation systems, inventions and patents, engineering ethics and decision making in engineering, scientific document preparation and presentation. Recognition and analysis of the new and significant in electronics and informatics engineering field research and development problems are taught. Skills to plan, perform and organize scientific work are exercised. Abilities to prepare and present public presentations and posters, work in a team, communicate with colleagues and be in charge of others work are developed. Students must complete all scheduled laboratory work. Students must complete at least 80% of the course laboratory according to the semester Lecture schedule.
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ELESM17106 3 credits
Master's Research Work 1
Module aim
After the search, analysis and generalization of literature on the selected topic to prepare a draft of Master Graduation Thesis task.
Module description
During the course Master’s Research Work 1, a field of research is selected; skills of information technology application, assessment and analysis of literature and data, planning of work, thoroughness in preparation of graphical and text documentation are mastered. After the search of scientific-technical information on the selected topic and its analysis, an individual or a group problematic task of Master Graduation Thesis is prepared.
obligatory
-
ELESM17108 9 credits
Signals and Signal Processing
Module aim
Increasing of knowledge and skills in theoretical analysis of signals and their processing methods in newest technology. Be able to explain the proposed solution, self-employed or diverse.
Module description
The course of Signals and Processing provides knowledge on the basic items of signal theory: the analysis techniques of signals changing in electronic circuits, dynamic signals mapping, geometrical methods of signal theory, orthogonal signals theory, methods of calculating the amount of information. The mathematical models of fixed range signals and their associated sampling theorem, analytical signal, Gilbert’s transformation are analyzed. During the study of discrete signals and their processing the students learn to make mathematical models of discrete signals and calculate the signal characteristics. The skills to analyze creation, processing and utilization of digital signals are acquired. Analog-digital and digital-analog converters, the fast Fourier transform and its applications, digital filtering algorithms in time and frequency domain, the digital device speed are analyzed. Students must complete all scheduled laboratory work.
Students must attend at least 60% of the practical exercises (practical work) and at least half of the lectures according to the semester schedule. -
ELESM17107 6 credits
Intelligent Systems
Module aim
To introduce for students the mathematical methods used in modern intelligent systems, the elementary elements that make up these models, and to help students acquire practical skills in choosing the most suitable solution for the selected task, arguing the appropriateness of the selected solution, distinguishing advantages and formulating a technical task for the implementation of the solution.
Module description
Knowledge is gained about intelligent systems based on artificial neural networks, evolutionary calculations or fuzzy logic, their composition and principles of operation. New concepts for the application of intelligent systems are analyzed, the choice of methods, efficiency measurements and comparative studies are critically evaluated. It is learned to independently create individual components of intelligent systems, to model intelligent systems or their parts with MATLAB software or in the Python environment, and to apply them to analyze and process sound, image and other signals of a technical nature. 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. -
ELKRM17104 6 credits
Mathematical Modelling Technologies
Module aim
To learn design and develop mathematical models of electronic circuits and systems using modern modeling technologies, critically analyze modeling results and draw conclusions.
Module description
Mathematical modelling technologies subject delivers knowledge about mathematical modelling, numerical methods, application of linear and nonlinear equations, matrices and differential equations for description and numerical modelling of electronic circuits and systems, numerical integration and differentiation, data processing, analysis and visualization.
Students must complete no less than 80% of the scheduled laboratory works -
ELESM17109 6 credits
Fundamentals of Research and Innovations
Module aim
To deliver knowledge about research, development and innovation systems, to develop scientific work planning, performing and organizing skills and scientific results public presentation abilities.
Module description
Fundamentals of Research and Innovations subject delivers knowledge about research, development and innovation systems, inventions and patents, engineering ethics and decision making in engineering, scientific document preparation and presentation. Recognition and analysis of the new and significant in electronics and informatics engineering field research and development problems are taught. Skills to plan, perform and organize scientific work are exercised. Abilities to prepare and present public presentations and posters, work in a team, communicate with colleagues and be in charge of others work are developed. Students must complete all scheduled laboratory work. Students must complete at least 80% of the course laboratory according to the semester Lecture schedule.
-
ELESM17106 3 credits
Master's Research Work 1
Module aim
After the search, analysis and generalization of literature on the selected topic to prepare a draft of Master Graduation Thesis task.
Module description
During the course Master’s Research Work 1, a field of research is selected; skills of information technology application, assessment and analysis of literature and data, planning of work, thoroughness in preparation of graphical and text documentation are mastered. After the search of scientific-technical information on the selected topic and its analysis, an individual or a group problematic task of Master Graduation Thesis is prepared.
2 Semester
obligatory
-
ELESM17207 9 credits
Real-Time Systems (with Course Project)
Module aim
Introduction to algorithms for adaptive real-time digital signal processing. Practical skills of team work in designing and implementing the algorithms on general-purpose digital signal processors.
Module description
Concepts of adaptive filtering. Basic Wiener filter theory. Most popular modifications of least mean squares algorithm. Design of adaptive real-time digital filters. Applications of adaptive filters in medicine, speech signal processing and for telephone echo cancellation. Implementation of the adaptive filtering algorithms on general-purpose digital signal processor. Students must complete all scheduled laboratory work.
Students must attend at least 60 (or 70, 80… ) per cent of the practical exercises (practical work) and at least half of the lectures according to the semester schedule. -
ELESM19204 6 credits
Speech Signal Processing
Module aim
To obtain knowledge about speech signal and its analysis, enhancement, coding, recognition and synthesis, to be able to deal with the practical and research speech processing tasks.
Module description
During studies the knowledge of the speech signal nature and properties, speech production and perception processes and models are obtained. Understanding of various classical and modern speech signal analysis, speech enhancement, compression, recognition, and synthesis techniques is acquired also. The obtained knowledge is deepened by studying textbooks and scientific publications, by preparing and presenting writing work on selected topic. Practical and research skills are obtained during laboratory work and project activities by solving speech processing problems and performing experimental studies. Students must complete all scheduled laboratory work. Students must attend at least 80% of the laboratory and at least half of the lectures according to the semester schedule.
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ELESM25201 6 credits
Advanced Digital Twin Systems
Module aim
xxx
Module description
Students must complete all scheduled laboratory work.
Students must attend at least 60 (or 70, 80… ) per cent of the practical exercises (practical work) and at least half of the lectures according to the semester schedule. -
ELESM17213 3 credits
Master's Research Work 2
Module aim
Prepare an analytical overview of scientific-technical literature on the selected topic, formulate master’s research aim and objectives, develop and approve individual or a group problematic task of Master Graduation Thesis.
Module description
During the course Master’s Research Work 2, knowledge in a chosen field of research is deepened; analytical review of scientific and technical information on the chosen topic is prepared; research aim and objectives for Master Graduation Thesis are formulated and an individual or a group problematic task of Master Graduation Thesis is approved.
obligatory
-
ELESM25202 9 credits
Text and Natural Language Processing (with Course Project)
Module aim
xx
Module description
Students must attend at least 60 (or 70, 80… ) per cent of the practical exercises (practical work) and at least half of the lectures according to the semester schedule.
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MEBIM17069 6 credits
Information Systems in Medicine
Module aim
Aims to equip students with an understanding of healthcare information systems, their applications, and the technologies shaping modern healthcare delivery
Module description
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.
-
ELESM25201 6 credits
Advanced Digital Twin Systems
Module aim
xxx
Module description
Students must complete all scheduled laboratory work.
Students must attend at least 60 (or 70, 80… ) per cent of the practical exercises (practical work) and at least half of the lectures according to the semester schedule. -
ELESM17213 3 credits
Master's Research Work 2
Module aim
Prepare an analytical overview of scientific-technical literature on the selected topic, formulate master’s research aim and objectives, develop and approve individual or a group problematic task of Master Graduation Thesis.
Module description
During the course Master’s Research Work 2, knowledge in a chosen field of research is deepened; analytical review of scientific and technical information on the chosen topic is prepared; research aim and objectives for Master Graduation Thesis are formulated and an individual or a group problematic task of Master Graduation Thesis is approved.
one of the following
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ELESM17211 6 credits
Algorithms and Data Structures
Module aim
To grasp fundamentals of one-dimensional and multidimensional data structures, to develop algorithms based on these data structures, and to apply these algorithms in structured and semistructured information search, data base indexing, digital image processing, computer graphics and vision, while being able to reason the chosen technical solutions.
Module description
In this course are presented logical and hierarchical data structures, sorting and search techniques and algorithms for sequence processing and compression. Reviewed applicability analysis of these basic algorithms for the construction of the more sophisticated application-oriented algorithms (structured and semistructured information search, data base indexing, digital image processing, computer graphics and vision).Effect of the nature of applications on algorithm formalization and their sophistication analysis is also presented. Students must complete all scheduled laboratory work.
Students must attend at least 80% of the laboratory and at least half of the lectures according to the semester schedule. -
ELEIM17200 6 credits
Systemotechnique and Sensors
Module aim
To analyze principles of work of gauges and converters, to be able to project circuits of the automated measurement and control.
Module description
Resistive, inductive, capacitor, photoelectric gauges used in power electronics. DAC and ADC. Microprocessors and use of him for processing the information and control.
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ELEIM17256 6 credits
Modern Electric Drives
Module aim
Acquire knowledge about performance and control methods of modern electric drives; learn to use them in practice. Learn to choose drive and its elements according to specification of technological process. Acquire knowledge about vector and direct torque and flux control, sensor-less control, Fuzzy control and principles of development of Fuzzy controllers, learn to develop computer models of electric drives, and ability to work individually and in a team.
Module description
Fundamental Knowledge of Modern Electric Drives, Their Structure and Characteristics, and Drive Control Methods: Vector Control of Induction Drives, Direct Torque and Flux Control. Application of Fuzzy Logic in Electric Drive Control, Principles of Fuzzy Controller Design, Control of DC and Induction Drives Using Fuzzy Controllers. Sensorless Drives. Drives for Modern Tracking and Positioning Systems, as well as Mathematical and Simulation Models of Electric Drives.
one of the following
-
ELESM17211 6 credits
Algorithms and Data Structures
Module aim
To grasp fundamentals of one-dimensional and multidimensional data structures, to develop algorithms based on these data structures, and to apply these algorithms in structured and semistructured information search, data base indexing, digital image processing, computer graphics and vision, while being able to reason the chosen technical solutions.
Module description
In this course are presented logical and hierarchical data structures, sorting and search techniques and algorithms for sequence processing and compression. Reviewed applicability analysis of these basic algorithms for the construction of the more sophisticated application-oriented algorithms (structured and semistructured information search, data base indexing, digital image processing, computer graphics and vision).Effect of the nature of applications on algorithm formalization and their sophistication analysis is also presented. Students must complete all scheduled laboratory work.
Students must attend at least 80% of the laboratory and at least half of the lectures according to the semester schedule. -
ELEIM17200 6 credits
Systemotechnique and Sensors
Module aim
To analyze principles of work of gauges and converters, to be able to project circuits of the automated measurement and control.
Module description
Resistive, inductive, capacitor, photoelectric gauges used in power electronics. DAC and ADC. Microprocessors and use of him for processing the information and control.
-
ELEIM17256 6 credits
Modern Electric Drives
Module aim
Acquire knowledge about performance and control methods of modern electric drives; learn to use them in practice. Learn to choose drive and its elements according to specification of technological process. Acquire knowledge about vector and direct torque and flux control, sensor-less control, Fuzzy control and principles of development of Fuzzy controllers, learn to develop computer models of electric drives, and ability to work individually and in a team.
Module description
Fundamental Knowledge of Modern Electric Drives, Their Structure and Characteristics, and Drive Control Methods: Vector Control of Induction Drives, Direct Torque and Flux Control. Application of Fuzzy Logic in Electric Drive Control, Principles of Fuzzy Controller Design, Control of DC and Induction Drives Using Fuzzy Controllers. Sensorless Drives. Drives for Modern Tracking and Positioning Systems, as well as Mathematical and Simulation Models of Electric Drives.
3 Semester
obligatory
-
ELESM17307 9 credits
Open Source Software for Science, Business and Management
Module aim
Get knowledge about Open source software licenses, usage, making information systems, using Open source software.
Module description
Studies of Open source software includes knowledge of design, licenses, distribution, usage. Students will learn how to find Open source software, make information systems using proprietary and Open source software. 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. -
ELESM17313 9 credits
Distributed Systems Engineering (with Course Project)
Module aim
To deliver master students theoretical knowledge, understanding and the practical skills necessary for research, creation and maintenance of distributed systems, and also to acquire respective design methods, algorithms of functioning and development tools.
Module description
Studying the subject Distributed System Engineering such knowledge are acquired: a principle of the distributed system operation, their characteristics, their development, design aspects, computer networks organization, inter-process communication, remote process calls, the distributed operating systems, naming aspects, processes synchronization, replications, data and hardware sharing, parallel information processing management, the distributed transactions, safety aspects. 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. -
ELESM17320 3 credits
Master's Research Work 3
Module aim
Accomplish research objectives stated in the task of Master Graduation Thesis and prepare Master’s Research Work 3 report.
Module description
During the course Master’s Research Work 3, theoretical and practical knowledge in a chosen research field is deepened; skills to make and/or use models of electronic information systems, analysis techniques and tools in order to solve objectives of Master Graduation Thesis are acquired; creativity, innovation, time management organizational and decision-making skills are mastered. The report on Master’s Research Work 3 is prepared, describing the research goals, objectives, motivated research methodology and software selection, evaluation and summary of the research results.
obligatory
-
ELESM19301 9 credits
Deep Learning Systems (with Course Project)
Module aim
To provide knowledge about Deep Learning: basics (layers, functions, and optimization algorithms), Deep Learning application for image analysis (classification, segmentation, object detection). Provide ability to reason the selection of suitable net architectures for different tasks, to understand and be able to analyze the results and causes. Learn to apply Deep Learning tools Python, Keras and Tensorflow.
Module description
Deep neural networks. Activation functions. Layer types: convolutional, normalization, averaging, max pooling, fully connected. Optimization algorithms: SGD, Adam, RMSprop. Metrics: accuracy, probabilistic, regression, image segmentation. Selection of architecture. Selection of training parameters. Visualization of filters. Image classification. Style transfer. Object detection. Students must complete all scheduled laboratory work. Students must attend at least 80% of the laboratory and at least half of the lectures according to the semester schedule.
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ELESM19304 9 credits
Computer Vision in Medicine
Module aim
To learn how to create and improve modern image analysis and computer vision tools based on machine learning technologies, to understand their working principles and medical applications, to be able to make reasoned choices when working individually or in groups.
Module description
The course provides knowledge of modern image analysis and computer vision tools, their principles of operation and medical applications. Introduction to the principles of computer vision, models of vision systems and their application. It reviews important mechanisms discovered in biological vision systems that can inspire the design and development of artificial vision systems. Linear and nonlinear digital image processing and analysis systems are analyzed, ranging from 2D finite impulse response filters to convoluted artificial neural networks, thus explaining how complex vision systems work. Knowledge of digital image processing and analysis tools, their development, modeling and application to medical image processing. Advanced open source frameworks for deep and machine learning algorithms (Tensorflow, PyTorch) are used for advanced image analysis during practical tasks. Students must complete all scheduled laboratory work. Students must attend at least 80% of the laboratory and at least half of the lectures according to the semester schedule.
-
ELESM17320 3 credits
Master's Research Work 3
Module aim
Accomplish research objectives stated in the task of Master Graduation Thesis and prepare Master’s Research Work 3 report.
Module description
During the course Master’s Research Work 3, theoretical and practical knowledge in a chosen research field is deepened; skills to make and/or use models of electronic information systems, analysis techniques and tools in order to solve objectives of Master Graduation Thesis are acquired; creativity, innovation, time management organizational and decision-making skills are mastered. The report on Master’s Research Work 3 is prepared, describing the research goals, objectives, motivated research methodology and software selection, evaluation and summary of the research results.
one of the following
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ELEIM17302 6 credits
Electromagnetic Pulse Power Technologies
Module aim
To get to know high-power devices, areas of their application, to learn to estimate limiting parameters of devices.
Module description
Introduction to electrical breakdown in gases, liquids and solid state materials, optically and electrically induced phase transitions in semiconductors, in superconductors, shock waves action on electrical conductivity of semiconductors, dielectrics, ferroelectrics and ferromagnetics and application of these effects for magnetic flux compression. High power electric and magnetic field values measurements and pulse-power electronics devices.
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ELESM19308 6 credits
Evolutionary Computation and Agent Systems
Module aim
To learn define, project, create, improve and apply various evolutionary computation algorithms and agent systems, know how to explain proposed solutions, analyze examples, while working in a group or individually.
Module description
Evolutionary computation and agent systems module develops knowledge of genetic algorithms, evolutionary strategies, genetic programing, agents, multi-agent systems and other metaheuristics algorithm based system elements and working principles. In the study course, skills for analyzing specific examples, defining and understanding advantages and disadvantages of evolutionary algorithms are developed. Additionally, skills to practically apply evolutionary computation and agent systems using MATLAB software is improved, along with skills to practically solve tasks which require artificial intelligence. Students must complete all scheduled laboratory work. Students must attend at least 80% of the laboratory and at least half of the lectures according to the semester schedule.
-
ELKRM17308 6 credits
Data Mining Techniques
Module aim
To provide knowledge about data mining methods used to reveal objectively existing patterns into various nature datasets, to develop the ability to select and apply the suitable data mining method for particular task.
Module description
Module content is focused on modern data mining techniques and their application to find previously unknown and potentially useful information analysing big data sets. Module content includes: data preprocessing and exploratory data analysis, classification methods (nearest neighbour’s method, naive Bayes classifier and decision trees), regression analysis, data clustering and evaluation of model performance.
Students must participate in no less than 80% of the scheduled practical works
one of the following
-
ELEIM17302 6 credits
Electromagnetic Pulse Power Technologies
Module aim
To get to know high-power devices, areas of their application, to learn to estimate limiting parameters of devices.
Module description
Introduction to electrical breakdown in gases, liquids and solid state materials, optically and electrically induced phase transitions in semiconductors, in superconductors, shock waves action on electrical conductivity of semiconductors, dielectrics, ferroelectrics and ferromagnetics and application of these effects for magnetic flux compression. High power electric and magnetic field values measurements and pulse-power electronics devices.
-
ELESM19308 6 credits
Evolutionary Computation and Agent Systems
Module aim
To learn define, project, create, improve and apply various evolutionary computation algorithms and agent systems, know how to explain proposed solutions, analyze examples, while working in a group or individually.
Module description
Evolutionary computation and agent systems module develops knowledge of genetic algorithms, evolutionary strategies, genetic programing, agents, multi-agent systems and other metaheuristics algorithm based system elements and working principles. In the study course, skills for analyzing specific examples, defining and understanding advantages and disadvantages of evolutionary algorithms are developed. Additionally, skills to practically apply evolutionary computation and agent systems using MATLAB software is improved, along with skills to practically solve tasks which require artificial intelligence. Students must complete all scheduled laboratory work. Students must attend at least 80% of the laboratory and at least half of the lectures according to the semester schedule.
-
ELKRM17308 6 credits
Data Mining Techniques
Module aim
To provide knowledge about data mining methods used to reveal objectively existing patterns into various nature datasets, to develop the ability to select and apply the suitable data mining method for particular task.
Module description
Module content is focused on modern data mining techniques and their application to find previously unknown and potentially useful information analysing big data sets. Module content includes: data preprocessing and exploratory data analysis, classification methods (nearest neighbour’s method, naive Bayes classifier and decision trees), regression analysis, data clustering and evaluation of model performance.
Students must participate in no less than 80% of the scheduled practical works
Free choice
Free choice
4 Semester
obligatory
-
ELESM17402 30 credits
Master Graduation Thesis
Module aim
Complete in the task of Master Graduation Thesis stated reseach, prepare the thesis, and demonstrate that competence and ability gained in education and research time correspond to Informatics Engineering master requirements.
Module description
During the course Master Graduation Thesis, chosen investigations are finalized and thesis is prepared. In the thesis the choice of research field is motivated, an analytical literature review, research aims and objectives, justification of the choice of investigation technique and equipment are provided, evaluation and summary of the research results are given. Moreover the presentation of results in the scientific conference is prepared, delivered for scientists audience evaluation and filed as the annexes of the thesis.
obligatory
-
ELESM17402 30 credits
Master Graduation Thesis
Module aim
Complete in the task of Master Graduation Thesis stated reseach, prepare the thesis, and demonstrate that competence and ability gained in education and research time correspond to Informatics Engineering master requirements.
Module description
During the course Master Graduation Thesis, chosen investigations are finalized and thesis is prepared. In the thesis the choice of research field is motivated, an analytical literature review, research aims and objectives, justification of the choice of investigation technique and equipment are provided, evaluation and summary of the research results are given. Moreover the presentation of results in the scientific conference is prepared, delivered for scientists audience evaluation and filed as the annexes of the thesis.
Statistics
| Metric | Value |
|---|---|
| Enrolled students | 7 |
| Enrolled to FT | 7 |
| Min FT grade | 8.91 |