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; 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

Sampling and Quantization Convolution, Correlation, Cross-Correlation Time Domain vs. Frequency Domain FFT, DCT Spectrogram MFCC z-Transform Kalman Filter

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]

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