- 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 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
- 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 (2 module)0.5 * exam + 0.18 * Homework 1 + 0.16 * Homework 2 + 0.16 * Homework 3
- 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
- James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.