2024/2025

Машинное обучение

Статус: Маго-лего

Кто читает: Департамент больших данных и информационного поиска

Когда читается: 3, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Саночкин Юрий Ильич

Язык: английский

Кредиты: 6

Course Syllabus

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