Master 2022/2023

Modern Data Analysis: Machine Learning

Type: Compulsory course

Area of studies: Applied Mathematics and Informatics

Delivered by: Big Data and Information Retrieval School

Where: Faculty of Computer Science

When: 2 year, 1 module

Mode of studies: distance learning

Online hours: 82

Open to: students of one campus

Instructors: Anastasia Maximovskaya

Master’s programme: Master of Data Science

Language: English

ECTS credits: 5

Contact hours: 8

Course Syllabus

Abstract

In this course, students are introduced to the main types of machine learning problems. Simple and multiple linear regression, evaluation of the quality of the obtained models, interpretation of the importance of features are considered. The solution of the classification problem is also discussed: algorithms such as Logistic Regression, Support Vector Machine and Decision Trees are considered. In addition, students gain insight into model ensembles.

Learning Objectives

• Get familiar with the basic machine learning definitions
• Understand such concepts as overfitting and regularization
• Understand how gradient descent works and how it is used in machine learning
• Know which models are used to solve regression and classification tasks
• Be able to use the scikit-learn library to train machine learning models

Expected Learning Outcomes

• Get familiar with the basic machine learning definitions
• Understand how gradient descent works and how it is used in machine learning.
• Understand such concepts as overfitting and regularization.
• Know which models are used to solve regression and classification tasks.
• Be able to use the scikit-learn library to train machine learning models.

Course Contents

• 2. Linear Regression and Gradient Descent
• 3. Overfitting and Regularization
• 4. Classification
• 5. Decision Trees
• 6. Ensembling
• 1. Introduction

Assessment Elements

• Quizzes
• Final Project
• Programming assignments
• Peer Review

Interim Assessment

• 2022/2023 1st module
  0.3 * Quizzes + 0.2 * Final Project + 0.125 * Peer Review + 0.375 * Programming assignments

Bibliography

Recommended Core Bibliography

• Foundations of machine learning, Mohri, M., 2012
• Introduction to machine learning, Alpaydin, E., 2020
• Linear regression analysis, Seber, G. A. F., 2003
• Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019

Recommended Additional Bibliography

• A first course in machine learning, Rogers, S., 2012