1. 
Title of the discipline (basic disciplines) 
Machine learning 
2. 
Course of Study 
1 course, 1 31 81 06 Web programming and Internet technologies 
3. 
Semester of training 
1 
4. 
Amount of credits 
5 
5. 
Full name of the lecturer 
Radyno Evgeniy Mefodievich 
6. 
Objectives of studying the discipline

Familiarization of students with the main modern methods of automatic detection and description of regularities in data from the surrounding world. Training methods of automating activities based on data analysis and modeling, without explicit prewriting action algorithms.
As a result of studying the discipline, the trainee must know:
be able to:

7. 
Prerequisites

Linear algebra, probability theory, mathematical statistics, fundamentals of mathematical analysis, the fundamentals of functional analysis, the fundamentals of programming 
8. 
Contents of the discipline

Types of variables (continuous, categorized, ordered). Precedent learning. Predictors and target variables. Problems of regression and classification. Probabilistic formulation of the classification problem. Steps of the analysis of data CRISPDM. Retraining of the model and ways to combat the reeducation of the model. Entropy and information. Density of distribution. The maximum likelihood method for estimating a parameter. Comparison of binary classifiers. ROCcurve. The loss function in assessing the quality of the model. Basic metrics for quality assessment. Linear regression and regularization (LASSO, Ridge). Generalized linear regression. Logistic regression. Trees in the problem of classification and regression. The nearestneighbor method. Support vector method. The principal component method. Composite models (Bagging). Neural networks (perceptron, LSTM). Clustering 
9. 
Recommended literature 
Basic

10. 
Teaching Methods 
Lectures, laboratory practice, SSI (students scientific investigation) 
11. 
Language of instruction 
Russian 
12. 
Conditions (requirements), routine monitoring 
Laboratory work (when examining an examination, current academic performance is taken into account with a coefficient of 0.3) 
13. 
Appraisal Form 
exam 