Infopaket

Fundamentals of Computational Intelligence

ОК 22

Обов’язкова дисципліна для ОП

First

2021/2022

4 Semester

4

Apply knowledge of the basic forms and laws of abstract and logical thinking, the basics of the methodology of scientific knowledge, the forms and methods of extracting, analyzing, processing and synthesizing information in the subject area of computer science. Use the methods of computational intelligence, machine learning, neural network and fuzzy data processing, genetic and evolutionary programming to solve problems of recognition, forecasting, classification, identification of control objects, etc. Apply methods and algorithms of computational intelligence and intelligent data analysis in the tasks of classification, forecasting, cluster analysis, finding associative rules using software tools to support multidimensional data analysis based on DataMining, TextMining, WebMining technologies. Apply models of knowledge representation in modern information systems, process audio, video and text information and numerical data, including using neural network technologies.

Full-time form

Know the basics of mathematical analysis, algebra and geometry, operations research, algorithmization and programming, probability theory. Be able to search for information and solve classic optimization problems methods. Possess elementary skills of algorithmization of the process of solving applied problems.

The study of the academic discipline is aimed at the acquisition by students of competencies in the field of solving problems of identification and optimization of complex, non-smooth, polyextreme dependencies, which are models of various types of processes, using both applied analytical software and independently developed programs, which will allow future specialists independently solve data processing problems in conditions of uncertainty. The program of the discipline "Fundamentals of Computational Intelligence" is structured in such a way as to teach students to analyze the surrounding processes, build their models, solve problems of identifying unknown dependencies and optimization problems, choosing effective methods for this and performing analysis of the obtained solutions and, if necessary , to make parametric adjustments.

7. Snytyuk V. (2020) Method of Deformed Stars for Multi-extremal Optimization. One- and Two-Dimensional Cases. In: Palagin A., Anisimov A., Morozov A., Shkarlet S. (eds) Mathematical Modeling and Simulation of Systems. MODS 2019. Advances in Intelligent Systems and Computing,vol 1019. Springer, Cham. 8. Snytyuk V., Antonevych M., Didyk A. Optimization of Functions of Two Variables by Deformed Stars Method // 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2019, pp. 475-480. 9. Snytyuk V., Tmienova N. Method of Deformed Stars for Global Optimization // 2020 IEEE 2nd International Conference on System Analysis & Intelligent Computing (SAIC), Kyiv, Ukraine, 2020, pp. 1-4.

Lectures, laboratory classes, individual study

Forms of evaluation: the level of achievement of all planned learning outcomes is determined by the results of writing a test, surveys, performing laboratory classes and tasks for individual study. After completing the relevant topics, a test is conducted. The condition for receiving a positive final grade in the discipline is to achieve at least 60% of the maximum possible number of points. Minimum for admission to the exam is 36 points. At the same time, it is mandatory to complete all laboratory work, independent work and control work (at least 60% of the maximum grade). For students who have not scored the recommended minimum during the semester, it is mandatory to complete at least 60% of the planned laboratory work, as well as to write a test and the maximum grade for which cannot exceed 40% final assessment (up to 40 points on a 100-point scale).

Ukrainian