Bachelor
2019/2020
Machine Learning
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Compulsory course (Applied Mathematics and Information Science)
Area of studies:
Applied Mathematics and Information Science
Delivered by:
Department of Informatics
When:
3 year, 2 module
Mode of studies:
offline
Language:
English
ECTS credits:
4
Contact hours:
80
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
- 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
- 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
- Types of tasks. Metric classifiers. Clustering AlgorithmsTypes of machine learning tasks. Metric classifiers. Clustering Algorithms
- Decision trees, linear classifiers. Neural networksDecision trees. Linear classifiers. Neural networks and deep learning
- Regression analysis, ensemble methods. Stochastic searchLinear regression. Polynomial regression. Displacement and dispersion. Ridge regression. Voting. Bootstrapping. Boosting, adaptive boosting, gradient boosting. Monte Carlo search. Simulated Annealing Algorithm. Genetic algorithm.
Interim Assessment
- Interim assessment (2 module)0.5 * exam + 0.18 * Homework 1 + 0.16 * Homework 2 + 0.16 * Homework 3
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.