Machine Learning for Predictive Analytics in Business

About the course

Subjects

Microeconomics Macroeconomics International economics Finance IT

Course content

Machine learning plays an important role in business operations such as fraud detection, sales forecasting, pricing and consumer segmentation. This course introduces business students to its principles and applications, with a focus on predictive analytics. It combines theory and practice, covering essential mathematical and statistical concepts while teaching Python programming from scratch. Each session blends lectures with workshops, and students are expected to bring their laptops for the practical exercises.

Preliminary assignment: A small assignment
Session 1: Introduction and getting started with Python
Session 2: Data manipulation using Python
Session 3: Data visualization in Python
Session 4: Linear regression
Session 5: Logistic regression
Midway assignment: A small assignment
Session 6: Neural networks
Session 7: K-nearest neighbors and naive Bayes
Session 8: Tree-based methods
Session 9: Support-vector machines
Session 10: Cluster analysis

See course description in course catalogue

What you will learn

By the end of this course students will be able to:

Understand the fundamental issues and challenges of machine learning.
Appropriately choose and appraise machine learning algorithms for predictive analytics in business.
Effectively process, summarize, and visualize business data using appropriate analytical tools and methods.
Develop and apply machine learning algorithms to address business challenges and support decision-making.
Critically assess the ethical and societal implications of applying machine learning in business and societal contexts.

Course prerequisites

A completed bachelor’s degree or equivalent is required. Fundamental mathematics and statistics will be reviewed during the course; however, students are expected to have some prior training in areas such as calculus, linear algebra and probability theory. No programming experience is necessary, as Python will be taught from scratch.

Facts

Level

Master

Type

Elective

ECTS

7.5

Code

CGMAI3003U

Teaching periods

Summer

Academic year

2025/2026

Status

Available places

Teaching language

English

Teaching Format

On campus

Exams

Written assignment
Individual exam, summer

Assessment type

7 point grading scale