1 
Course topic 
Machine Learning 
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Specialty 
131 81 08 Computer mathematics and systems analysis 
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Year of study 
2 
4 
Academic semester 
3 
5 
Study credits 
5 
6 
Lecturer 
Goloubeva Larissa L., Ph.D., Associate Professor 
7 
Course purposes 
Training of specialists able to use fundamental mathematical knowledge as a basis for performing applied research in the field of data processing and artificial intelligence. 
8 
Prerequisite(s) 
Courses of disciplines “Algebra and Number Theory”, “Programming Methods and Informatics”, “Computer Mathematics”, “Neural networks and genetic algorithms”, “Theory of probability”, “Mathematical statistics”. 
9 
Course overview 
This course provides a broad introduction to machine learning. Topics include: learning theory, types of machine learning (supervised learning, unsupervised learning, semisupervised learning); methods of machine learning (Neural Networks, SVM, kNearest Neighbor kNN, Decision Tree, Cluster Analysis). The modern applications of machine learning are considered in the course. 
10 
Recommended literature 

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Teaching methods 
Mixed with elements of distance learning, problematic, research. 
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Language 
Russian 
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Forms of knowledge monitoring 
Preparation of reports, presentation on a given topic. Laboratorypractical control (laboratory works, homework assignments), oral and written control (oral surveys, short class tests, test papers). 
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The knowledge check 
Credit, exam 