Computational Intelligence

Major: Data Science
Code of subject: 7.124.03.O.002
Credits: 6.00
Department: Information Systems and Networks
Lecturer: Doctor of Sciences., Professor Peleshchak Roman Mykhaylovych
Semester: 1 семестр
Mode of study: денна
Learning outcomes: 1. To know and be able to apply in practice the methods of system analysis, methods of mathematical and information modeling for the construction and study of models of objects and processes of informatization. 2. Know the methods of revealing uncertainties in the problems of systems analysis, be able to reveal situational uncertainties and uncertainties in the problems of interaction, counteraction and conflict of strategies, find a compromise in revealing conceptual uncertainty, etc. 3. To know and be able to apply methods of evolutionary modeling and genetic methods of optimization, methods of inductive modeling and mathematical apparatus of fuzzy logic, neural networks, game theory and distributed artificial intelligence, etc. 4. Know and be able to identify (evaluate) the parameters of mathematical models of control objects in real time in terms of changes in its dynamics and the action of random perturbations, using the measured signals of input and output coordinates of the object. 5. Know the models, methods and algorithms for decision-making in conflict, fuzzy information, uncertainty and risk. 6. Ability to search for information in the specialized literature in the field of systems analysis, using a variety of resources: journals, databases, on-line resources.
Required prior and related subjects: Systems Analysis, Simulation of complex systems, Machine Learning
Summary of the subject: The following topics are considered in the teaching of the discipline: Architecture of artificial neural networks. Neural network learning algorithms. Neural networks with feedback and self-organization. Fuzzy inference systems. Fuzzy neural networks and their use in prediction problems. The use of systems with fuzzy logic and fuzzy neural networks in forecasting problems in macroeconomics and financial analysis. Fuzzy neural networks in classification problems. Recognition of objects of electro-optical images using fuzzy neural networks. Method of inductive modeling in data mining problems. Cluster analysis in intelligent systems. Fuzzy cluster analysis algorithms. Genetic algorithms and evolutionary modeling. Evolutionary programming. Swarm algorithms of computational intelligence. Hybrid swarm optimization algorithms.
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, oral examination; • Final control (60%). in written – 50%, verbally- 10%.
Recommended books: 1. Згуровський М. З., Зайченко Ю. П. Основи обчислювального інтелекту. – Київ: Науково-виробниче підприємство «Видавництво «Наукова думка» НАН України» 2013, 407 с. 2. Harrison Kinsley, Daniel Kukiela. Neural Networks from Scratch in Python. Sentdex, Kinsley Enterprises Inc. 2020, 658 p. https://nnfs.io/ 3. Зайченко Ю. П. Основи проектування інтелектуальних систем. Навчальний посібник. – К.: Видавничий Дім «Слово», 2004. – 352 с. 4. Руденко О. Г., Бодянський Є. В. Штучні нейронні мережі: Навчальний посібник. – Харків: ТОВ «Компанія СМІТ», 2006. – 404 с. 5. М.А. Новотарський, Б.Б. Нестеренко. Штучні нейронні мережі: обчислення // Праці Інституту математики НАН України. – Т50. – Київ: Ін-т математики НАН України, 2004. – 408 с. 6. Пелещак Р.М., Литвин В.В., Пелещак І.Р. Методичний посібник для лабораторних робіт з навчальної дисципліни «Обчислювальний інтелект», 2021, 70 с.