Machine Learning I

2023/2024

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Educational Programmes

Course type:

Compulsory course

When:

1 year, 4 module

Instructors

Martinez-Saito, Mario

Юсупова Элен Магомедовна

Course Syllabus

Abstract

Prerequisite: Basic experience in Python. Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is valuable part of modern scientific experiments, in which results could be explained and analyzed, and substantiated with eligible proof. The course gives an introduction to the main topics of modern data analysis such as classification, regression, clustering, dimensionality reduction, scalable algorithms. Each topic is accompanied by a survey of key machine learning algorithms solving the problem and is illustrated with a set of real-world examples. The goal of this course is to teach students to work with, analyze and apply it to data obtained during experiments that evaluate biological, behavioural, neuroimaging parameters, or a combination of these.

Learning Objectives

• Learning the basic concepts and methods used in Machine Learning.

Expected Learning Outcomes

• Get a glimpse of the state of affairs in machine learning and review logistic classification
• Learn to implement gradient descent and regularization, and apply support vector machines
• Learn the operation and training of neural networks, and their relation to deep learning
• Grasp the basics and how to run decision tree learning models, and scalable implementations
• Getting acquainted with the main unsupervised learning teachniques
• Learn the basic concepts and uses of reinforcement learning algorithms
• Read articles devoted to various applications of ML in education and analyze them

Course Contents

• Introduction
• Classification
• Neural networks: predicting
• Neural networks: learning
• Applied machine learning and decision trees
• Reinforcement learning

Assessment Elements

• Midterm
• Final exam
• Attendance and participation

Interim Assessment

• 2023/2024 4th module
  0.3 * Attendance and participation + 0.4 * Final exam + 0.3 * Midterm

Bibliography

Recommended Core Bibliography

• The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017

Recommended Additional Bibliography

• Pattern recognition and machine learning, Bishop, C. M., 2006