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Machine Learning

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
5
Кредиты
Статус:
Курс обязательный
Когда читается:
3-й курс, 2 модуль

Преподаватели

Course Syllabus

Abstract

The purpose of mastering the discipline is to familiarize students with the theoretical foundations and basic principles of machine learning, mastery tools, models and methods of machine learning, as well as the acquisition of skills as a data scientist and developer of mathematical models, methods and algorithms for data analysis. To master the discipline students must know linear algebra and geometry, basics of programming, differential equations and theory of probability and mathematical statistics.
Learning Objectives

Learning Objectives

  • The goal of mastering the discipline "Machine Learning" is to develop students 'theoretical knowledge and practical skills on the basics of machine learning, mastering students' tools, models and methods of machine learning, as well as acquiring the skills of a data scientist and developer of mathematical models, methods and analysis algorithms data.
Expected Learning Outcomes

Expected Learning Outcomes

  • Knows and knows how to work with various types of regressions. Conducts an analysis. Knows ensemble methods, stochastic search and algorithms
  • Knows rules and quality analysis; ROC curve analysis; algorithm for constructing decision trees; informational gain criterion and Gini criterion; forests of decisive trees. Knows transition to space of increased dimension; support vector method. Knows what is: logistic regression; gradient descent; neural networks and gradient backpropagation algorithm.
  • Knows the subject and tasks of machine learning and data analysis; basic principles, tasks and approaches, use in various fields of science and industry; the main stages of the evolution of machine learning algorithms. He knows the general form of the metric classifier, selection algorithms, clustering algorithms with a fixed number of clusters, density clustering algorithms, hierarchical clustering.
Course Contents

Course Contents

  • Types of tasks. Metric classifiers. Clustering Algorithms
  • Decision trees, linear classifiers. Neural networks
  • Regression analysis, ensemble methods. Stochastic search
Assessment Elements

Assessment Elements

  • non-blocking Homework 1
  • non-blocking Homework 2
  • non-blocking Homework 3
  • blocking exam
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.15 * Homework 1 + 0.15 * Homework 2 + 0.15 * Homework 3 + 0.55 * exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Флах П. - Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных - Издательство "ДМК Пресс" - 2015 - ISBN: 978-5-97060-273-7 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/69955

Recommended Additional Bibliography

  • James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.