3 semester

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Course topic

Machine Learning

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Specialty

1-31 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

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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.

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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”.

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Course overview

This course provides a broad introduction to machine learning. Topics include: learning theory, types of machine learning (supervised learning, unsupervised learning, semi-supervised learning); methods of machine learning (Neural Networks, SVM, k-Nearest Neighbor k-NN, Decision Tree, Cluster Analysis). The modern applications of machine learning are considered in the course.

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Recommended literature

  1. Хайкин, С. Нейронные сети: Полный курс, 2-е издание. М., «Вильямс», 2006. 1104 с.
  2. Haykin, S. Neural Networks and Learning Machines Third Edition. Copyright © 2009 by Pearson Education, Inc., Upper Saddle River, New Jersey 07458, 2009. 938 p.
  3. Beale, M.D. Neural Network Toolbox™. Getting Started Guide / M.D. Beale, M.T. Hagan, H.D. Demuth. © COPYRIGHT 1992–2017 by The MathWorks, Inc., 2017. 134 p.
  4. Beale, M.D. Neural Network Toolbox™. User’s Guide / M.D. Beale, M.T. Hagan, H.D. Demuth. © COPYRIGHT 1992–2017 by The MathWorks, Inc., 2017. 512 p.
  5. Daumé, H. A course in Machine Learning. http://ciml.info/dl/v0_9/ciml-v0_9-all.pdf
  6. Воронцов, К.В. Машинное обучение. Курс лекций. http://www.machinelearning.ru
  7. Флах, П. Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных. М.: ДМК Пресс, 2015. 400 с.

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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. Laboratory-practical 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