Bachelor
2022/2023
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
Open to:
students of one campus
Language:
English
ECTS credits:
5
Contact hours:
48
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
- 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 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
- Types of tasks. Metric classifiers. Clustering Algorithms
- Decision trees, linear classifiers. Neural networks
- Regression analysis, ensemble methods. Stochastic search
Assessment Elements
- Homework 1Homework 1 is given to students in one version and consists of 3 tasks. Each task is assigned a score. Homework completion time is 2 weeks.
- Homework 2Homework 2 is given to students in one version and consists of 4 tasks. Each task is assigned a score. Homework completion time is 2 weeks.
- Homework 3Homework 3 is given to students in one version and consists of 4 tasks. Each task is assigned a score. Homework completion time is 2 weeks.
- ExamThe exam is conducted in the form of answers to the questions of the exam ticket. The exam ticket contains two questions from the list of questions for the exam.
Interim Assessment
- 2022/2023 2nd moduleGAccumulated = (Gh/w1 + Gh/w2 + Gh/w3) / 3 The resulting grade for the discipline is calculated as follows: GResultant = 0.5 Accumulated + 0.5 Exam
Bibliography
Recommended Core Bibliography
- 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.
Recommended Additional Bibliography
- James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.