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Бакалавриат 2019/2020

Машинное обучение

Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус: Курс обязательный (Прикладная математика и информатика)
Направление: 01.03.02. Прикладная математика и информатика
Когда читается: 3-й курс, 2 модуль
Формат изучения: без онлайн-курса
Преподаватели: Брыксин Тимофей Александрович, Литвинов Юрий Викторович, Шпильман Алексей Александрович
Язык: английский
Кредиты: 4

Course Syllabus

Abstract

It is a discipline of the basic part of the professional cycle. The purpose of mastering the discipline is to familiarize students with the theoretical foundations and basic principles of machine learning, mastery of students' 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. As a result of mastering the discipline, the student must: know: key concepts, goals and objectives of using machine learning; methodological foundations of the application of machine learning algorithms; be able to: visualize the results of machine learning algorithms, choose a machine learning method appropriate to the research task, interpret the results; own: the skills of reading and analyzing academic literature on the application of machine learning methods, building and evaluating the quality of models.
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 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.
  • 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 and knows how to work with various types of regressions. Conducts an analysis. Knows ensemble methods, stochastic search and algorithms
Course Contents

Course Contents

  • Types of tasks. Metric classifiers. Clustering Algorithms
    Types of machine learning tasks. Metric classifiers. Clustering Algorithms
  • Decision trees, linear classifiers. Neural networks
    Decision trees. Linear classifiers. Neural networks and deep learning
  • Regression analysis, ensemble methods. Stochastic search
    Linear regression. Polynomial regression. Displacement and dispersion. Ridge regression. Voting. Bootstrapping. Boosting, adaptive boosting, gradient boosting. Monte Carlo search. Simulated Annealing Algorithm. Genetic algorithm.
Assessment Elements

Assessment Elements

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

Interim Assessment

  • Interim assessment (2 module)
    0.5 * exam + 0.18 * Homework 1 + 0.16 * Homework 2 + 0.16 * Homework 3
Bibliography

Bibliography

Recommended Core Bibliography

  • Флах П. - Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных - Издательство "ДМК Пресс" - 2015 - 400с. - 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.