Infopaket

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.

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.

1. Zgurovskiy M.Z., Zaychenko Yu.P. The Fundamentals of Computational Intelligence: System Approach. – Springer, 2017. – 395 p. 2. Haykin S. Neural Networks and Learning Machines Third Edition / Pearson. Prentice Hall, 2009. – 938p. 3. Russel S., Norvig P. Artificial Intelligence: A Modern Approach (Pearson Series in ArtificalIntelligence) 4th Edition / Pearson, 2020. – 1136 p. 4. Snytyuk V., Suprun O. Evolutionary clustering as technique of economic problems solving //Electronic and control Systems. – 2017. − No 4 (54). – P. 95-101.

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. The specific weight of learning outcomes in the final assessment, provided they are mastered at the appropriate level, is as follows: 1. Verification work:— 18%, 2. Laboratory classes (reports, 7): —7% each, 3. Individual study (presentation, about scientist in the field of CI):—5%, 4. Individual study (presentation, new topic): - 8%, 5. Survey (test, 10): - 2% each. Assessment of students is carried out during the semester for all types of work. The total score for the semester is formed as a weighted sum of points earned by the student for various types of work (presented as a percentage of the maximum possible score that can be earned in the semester, namely 60 points). Total score = control work (18%) + laboratory works (49%) + individual study (13%) + surveys (20%).

Ukrainian