As a result of the study of the discipline, the student should be able to demonstrate the following learning outcomes: to know: application areas and basic applied aspects of machine learning; basic concepts and principles of work of artificial neural networks; problem statement and basic natural language processing methods; be able to: correctly formulate the tasks that arise in the practical activity, to solve them by means of machine learning methods; to analyze a specific problem in order to select the best method of machine learning for its solution; to carry out the analysis and synthesis of informative features; to analyze the work of machine learning methods to identify their strengths and weaknesses

Discrete Math
Mathematical analysis
Linear algebra
Probability theory
Mathematical statistics

The purpose of studying the discipline is to obtain the necessary knowledge and to acquire practical skills in applying a wide range of methods and algorithms for analyzing information in the context of machine perception and learning to understand the issues of building, operating and operating computer systems and networks, as well as various information processing and control systems basis.

Stephen Marsland. Machine Learning). Лінійна: An Alg). Лінійнаorithmic Perspective, 452 р.,
2015.
Ethem Alpaydin. Introduction To Machine Learning). Лінійна, 584 p., 2009.
Tom M. Mitchell. Machine Learning). Лінійна
[http://www.cs.cmu.edu/~tom/mlbook.html]
Yaser S. Abu-Mostafa. Learning). Лінійна from data, 215 p., 2017

Current control
Laboratory work 40 points
Examination control
Written component of 60 points
Oral component 0 points
Total 100 points