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

Image Kernel Operations Image Clustering and Segmentation Image Feature Extraction (SIFT, HOG) Convolutional Neural Networks Object Detection Object Spatial Localization Object Classification Optical Flow and Tracking CNNs for Video

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