Machine Learning

Master 2021/2022

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 (Machine Learning and Data Analysis)

Area of studies: Applied Mathematics and Informatics

Delivered by: Department of Informatics

Where: St. Petersburg School of Physics, Mathematics, and Computer Science

When: 2 year, 1, 2 module

Mode of studies: offline

Open to: students of one campus

Instructors: Aleksei Shpilman

Master’s programme: Machine Learning and Data Analysis

Language: English

ECTS credits: 3

Contact hours: 56

Full Syllabus Ask Question

Abstract

It is a compulsory discipline. The purpose 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. As a result of mastering the discipline, the student must: - Know the 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, and interpret the results. - Have the skills (gain experience) of reading and analyzing academic literature on the application of machine learning methods, building and evaluating the quality of models.

Learning Objectives

1. The formation of students' theoretical knowledge and practical skills on the basics of machine learning.
2. Students mastering tools, models and methods of machine learning
3. Acquiring the skills of a data scientist and developer of mathematical models, methods and algorithms for data analysis.

Expected Learning Outcomes

Fluent in concepts: linear regression; polynomial regression; displacement and dispersion
Has skills in algorithms. Knows: clustering algorithms with a fixed number of clusters; density clustering algorithms.
Has skills in algorithms. Owns the concepts of: Monte Carlo Searches; simulated annealing algorithm; genetic algorithm.
Knows: basic concepts and tasks of machine learning and data analysis; basic principles, tasks and approaches, use in various fields of science and industry.
Knows: general view of the metric classifier; K nearest neighbors algorithm; sampling algorithms.
Knows: rules and quality analysis (accuracy, completeness). Possesses analysis skills using the ROC curve. He knows the algorithm for constructing decision trees; informational gain criterion and Gini criterion.
Owns concepts: ridge regression.
owns concepts: voting; bootstrapping; boosting, adaptive boosting, gradient boosting.
Owns the concepts of: logistic regression; gradient descent; neural networks and gradient backpropagation algorithm
Owns the concepts of: perceptron and dividing hyperplane. Owns concepts: transition to space of increased dimension. Knows the support vector method
To be able to visualize the results of machine learning algorithms, to choose a machine learning method that matches the research task and interpret the results.
To have the skills (to gain experience) of reading and analyzing academic literature on the application of machine learning methods, building and assessing the quality of models.
To know the key concepts, goals and objectives of using machine learning; methodological foundations for the application of machine learning algorithms.

Course Contents

Types of Machine Learning Tasks
Clustering Algorithms
Linear Classifiers
Metric classifiers
Decision trees
Neural networks and deep learning
Regression analysis
Ensemble Methods
Stochastic search

Assessment Elements

Homework №1
Homework №2
Homework №3
Exam (3 module)
Экзамен проводится офлайн.
Exam (4 module)
Project
Students 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
The 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.

Interim Assessment

2020/2021 3rd module
0.59 * Homework №1 + 0.41 * Exam (3 module)
2020/2021 4th module
0.25 * Homework №2 + 0.5 * Exam (4 module) + 0.25 * Homework №3
2021/2022 2nd module
0.5 * Project + 0.5 * Exam

Bibliography

Recommended Core Bibliography

Murphy, K. P. (2012). Machine Learning : A Probabilistic Perspective. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=480968
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.
Trevor Hastie, Robert Tibshirani , et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, 2017. Free from the publisher: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

Authors

SHPILMAN ALEKSEY ALEKSANDROVICH

Course Syllabus