2025/2026

Machine Learning in Finance

Type: Optional course (faculty)

Delivered by: Undergraduate Programmes Curriculum Support

Where: Faculty of Economic Sciences

When: 2 module

Open to: students of one campus

Language: English

ECTS credits: 3

Contact hours: 32

Course Syllabus

Abstract

The aim of this course is to introduce students to the fundamental concepts of supervised machine learning (ML) and its applications in finance. During the lectures, we will cover key ML methods—including classification and regression trees, ensemble techniques, and artificial neural networks—without delving deeply into technical details (though several proofs will be presented, and a solid mathematical background is required to follow the material). In homework assignments, students will learn how to apply these methods to financial problems such as index trading, derivative pricing, volatility forecasting, and portfolio selection. Students will also gain practical experience in working with financial data using ML techniques. In addition, participants will learn basic Python commands and complete practical exercises.