Infopaket

Selected Topics in Artificial Intelligence

ВК.3.02.02

Вибіркова дисципліна для ОП

Second

2023/2024

4 Semester

3

PLO1. Be able to use of in-depth professional knowledge and practical skills to optimize the design of models of any complexity, to solve specific problems of designing intelligent information systems of different physical nature. PLO3. Gaining knowledge for the ability to evaluate existing technologies and on the basis of analysis to form requirements for the development of advanced information technologies. PLO5. Be able to carry out effective communicative activities of the project development team. PLO8. Communicate effectively on information, ideas, problems and solutions with professionals and society at large. PLO10. Be able to build models of physical and production processes, design of storage and data space, knowledge base, using charting techniques and standards for information systems development.

1. Knowledge of: chapters of mathematical analysis, fundamentals of artificial intelligence, differential equations, methods of function optimization, programming theory, mathematical logic, and mathematical statistics. 2. Ability to: formulate and solve problems of linear programming, model dynamic systems, solve systems of linear algebraic equations with parameters, solve differential equations, and investigate functions and functionals for extrema. 3. Proficiency in skills of: constructing, analyzing, and applying mathematical models in solving applied problems.

The aim of the course is for students to master methods and approaches to developing artificial intelligence tools in specialized computer systems, and to gain knowledge of selected topics in artificial intelligence: the fundamentals of fuzzy logic, its role and connection with neural networks, and its application in the design of decision-making and control systems.

1. Zaichenko Yu.P. Nechitki modeli i metody v intelektualnykh systemakh. K.: Vydavnychyi Budynok «Slovo»", 2008. – 344 p. 2. Kutkovetskyi V. Ya. Rozpiznavannia obraziv : navchalnyi posibnyk / V. Ya. Kutkovetskyi. – Mykolaiv : Vyd-vo MDHU im. P. Mohyly, 2003. – 196 p. 3. Bellman R., Zadeh L. Decision-Making in Fuzzy Environment Science. – 1970. – Vol.17. – No. 4. – P.141-160. 4. Lotfi Zadeh: From computing with numbers to computing with words – from manipulation of measurements to manipulation of perceptions in International Journal of Applied Math and Computer Science, pp. 307-324, vol. 12, no. 3, 2002. 5. Fuzzy Logic Toolbox. Users Guide, Version 2.1 The MathWorks, Inc., 2001.

Lectures, laboratory work, independent work.

Semester Assessment: The maximum number of points a student can earn is 60. 1. Laboratory work 1: 40/24 points. 2. Ongoing assessment: 20/12 points. Final assessment (in the form of an exam): - Maximum number of points a student can earn: 40 points. - Format: written; - Types of tasks: 3 written tasks. A student is not admitted to the exam if they have earned fewer than 36 points during the semester.

Ukrainian