- 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.
- 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.
- Types of tasks. Metric classifiers. Clustering Algorithms
- Decision trees, linear classifiers. Neural networks
- Regression analysis, ensemble methods. Stochastic search
- Homework №1
- Homework №2
- Homework №3
- Exam №2
- ProjectStudents must apply the passed methods to parse one of the articles on machine learning. Any publications of machine learning conferences can be selected as articles.
- Exam №1The exam is conducted in the format of parsing a scientific article on data analysis and machine learning. The examinee must demonstrate knowledge of the subject at a sufficient level to interpret contemporary scientific literature.
- 2021/2022 3rd module0.5 * Homework №1 + 0.5 * Homework №2
- 2021/2022 4th moduleThe teacher takes into account the assessment for the current control (homework). The resulting grade for the discipline is calculated as follows: Resultant = 0.5 Accumulated + 0.5 Exam
- 2022/2023 2nd module0.5 * Project + 0.5 * Exam №1
- Флах, П. Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных / П. Флах. — Москва : ДМК Пресс, 2015. — 400 с. — ISBN 978-5-97060-273-7. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/69955 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
- James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.