Applied Machine Learning

About the course

Subjects

Statistics Quantitative methods Mathematics

Course content

The course will cover a range of machine learning (ML) methods with a particular emphasis on their practical applications. While the focus is on applied aspects, all methods will be introduced in a rigorous and precise manner. The topics to be covered include:

1. Overview of statistical learning

2. Linear regression

3. Classification

4. Resampling methods

5. Model selection and regularization

6. Moving Beyond Linearity

7. Tree-based methods

8. Deep learning

9. Survival analysis and censored data

10. Unsupervised learning

11. Multiple testing

See course description in course catalogue

What you will learn

The goal of the course is to demonstrate how machine learning (ML) methods can be applied across a variety of business domains, including econometrics, finance, macroeconomics, and management. The emphasis is on the practical application of these methods, with case studies designed to illustrate when and why a particular method is (or is not) suitable for a given type of data. Students will:

Be able to differentiate between different data types
Identify which method(s) are suitable to apply for a particular data problem
Understand basic theoretical concepts of machine learning
Be able to effectively interpret the results when applying different machine learning methods
Be aware of modern machine learning methods in prediction and causal inference applications

Course prerequisites

Familiarity with basic statistics concepts (probability, expectation) and basic regression analysis is assumed, and prior experience with the statistical software R or Python is advantageous.

Facts

Level

Bachelor

Type

Elective

ECTS

7.5

Code

BHAAV2624U

Teaching periods

Autumn – semester

Academic year

2026/2027

Status

Available places

Teaching language

English

Teaching Format

Hybrid

Exams

Written assignment
Individual exam, winter

Assessment type

7 point grading scale