Машинное обучение

Магистратура 2024/2025

Статус: Курс обязательный (Магистр по наукам о данных)

Кто читает: Департамент больших данных и информационного поиска

Когда читается: 1-й курс, 3, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Саночкин Юрий Ильич

Язык: английский

Full Syllabus Ask Question

Abstract

The Machine Learning course provides fundamental knowledge and practical skills in the field of artificial intelligence and data analysis. It covers the basic methods and algorithms used to build models capable of learning from data and making decisions.

Learning Objectives

To introduce students to the key concepts and techniques of machine learning.
To develop the skills of choosing and applying the most suitable algorithms for solving specific tasks.
Teach you how to analyze, process, and interpret large amounts of data.
Develop skills to evaluate and improve the quality of machine learning models.

Expected Learning Outcomes

Selects and applies suitable machine learning algorithms for different types of tasks
Performs data preprocessing and analysis, including cleaning, normalization, and processing of missing values.
Develops, trains, and evaluates classification and regression models.
Applies clustering methods in practice to group data.
Evaluates the quality of models using various metrics and cross-validation methods.
Owns tools and frameworks for machine learning, such as scikit-learn and others.
It takes into account the ethical aspects of using machine learning in model development.
Integrates machine learning models into real business processes and solves practical problems in various fields.

Course Contents

The Numpy Library
The Numpy Library 2
The Pandas Library
The Pandas Library 2
Pandas for advanced data analysis tasks
Pandas for advanced data analysis tasks 2
Introduction to Machine learning
Introduction to Machine learning 2
An introduction to machine learning. A practical case.
Mathematics for data analysis. Linear algebra.
A linear algebra workshop
Mathematics for data analysis. Mathematical analysis.
Mathematical Analysis Workshop
The task of classification. KNN.
The task of classification. KNN. 2
The regression task. KNN and linear regression.
The regression task. KNN and linear regression. 2
Decision Trees
Decision Trees 2
Compositions above the trees. A random forest.
Compositions above the trees. A random forest. 2
Compositions above the trees. Gradient boosting.
Compositions above the trees. Gradient boosting. 2
The clustering task
The clustering task 2

Assessment Elements

Homeworks (HW)
Group Project (GP)
Exam (EX)
Activity in lectures and seminars (ACT)

Interim Assessment

2024/2025 4th module
Min (0,3 HW + 0,3 GP + 0.4 EX + 0.2 ACT; 10), Min - mathematical minimum function

Bibliography

Recommended Core Bibliography

Grus, J. (2019). Data Science From Scratch : First Principles with Python (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2102311
Machine learning : a probabilistic perspective, Murphy, K. P., 2012
Pattern recognition and machine learning, Bishop, C. M., 2006
Машинное обучение. - 978-5-496-02989-6 - Бринк Хенрик, Ричардс Джозеф, Феверолф Марк - 2018 - Санкт-Петербург: Питер - https://ibooks.ru/bookshelf/355472 - 355472 - iBOOKS

Recommended Additional 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

Authors

Ахмедова Гюнай Интигам кызы

Course Syllabus