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Regular version of the site
Master 2024/2025

Modern Data Analysis: Machine Learning

Type: Compulsory course (Master of Data Science)
Area of studies: Applied Mathematics and Informatics
When: 2 year, 1 module
Mode of studies: distance learning
Online hours: 60
Open to: students of one campus
Master’s programme: Master of Data Science
Language: English
ECTS credits: 3
Contact hours: 12

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

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

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

Course Contents

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

Assessment Elements

  • non-blocking Quizzes
  • non-blocking Programming assignments
  • non-blocking Final Project
Interim Assessment

Interim Assessment

  • 2024/2025 1st module
    0.4 * Final Project + 0.3 * Programming assignments + 0.3 * Quizzes
Bibliography

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

Authors

  • Боднарук Иван Иванович