Infopaket

Models and algorithms in artificial intelligence tasks

ДВС.3.03.03

Вибіркова дисципліна для ОП

First

2022/2023

5 Semester

4

LO 8. Combine methods of mathematical and computer modeling with informal expert analysis procedures to find optimal solutions.

Distance form

Know control theory, and methods of mathematical modeling. Be able to apply knowledge of control theory and mathematical modeling methods. Possess elementary skills: solve problems in control theory and mathematical modeling methods.

Mastering the basic methods and means of solving artificial intelligence problems, regardless of their nature, as well as mastering skills and their use. The discipline has the following sections: neural networks of various types, mathematical models and algorithms in artificial intelligence tasks, and machine learning methods. The main task is to provide students with basic knowledge of the entire arsenal of methods and tools for key models and algorithms of artificial intelligence problems and to gain experience in working with neural networks and relevant software in solving applied problems. Uses concepts from the theory of machine learning, neural networks, mathematical analysis, and algebra.

1. Trushevskyi V.M., Shynkarenko H.A., Shcherbyna V.M. Metod skinchennykh elementiv i shtuchni neironni merezhi. Lviv: LNU imeni Ivana Franka, 2014. - 396 p. 2. Dovhiy S.O., Liashko S.I., Cherniy D.I. Alhorytmy metodu dyskretnykh osoblyvostei dlia obchysliuvalnykh tekhnolohii // Kybernetyka y systemnыi analyz. 2017, №6, pp.147-159. 4. Hlybovets M.M., Oletskyi O.V. Systemy shtuchnoho intelektu. — K.: KM Akademiia, 2002. - 366 p. 5. Rudenko O. H., Bodianskyi Ye. V. Shtuchni neironni merezhi: Navchalnyi posibnyk. — Kharkiv: TOV "Kompaniia SMIT", 2006. — 404 p. 6. Subbotin S.O. Podannia y obrobka znan u systemakh shtuchnoho intelektu ta pidtrymky pryiniattia rishen. Zaporizhzhia: ZNTU, 2008. — 341 p.

Lectures, practical classes.

Semester assessment: 1. Tests, current assessment: 80 points/48 points. 2. Current assessment, independent work: 20 points/12 points. The credit is given based on the results of the student's work throughout the entire semester and does not include additional assessment measures for successful students.

Ukrainian