Machine Learning for Business Analytics (ML4BA)

Welcome to Machine Learning for Business Analytics! :-)

In today’s data-driven world, the ability to turn information into insight is a defining advantage. This course bridges the gap between modern machine learning techniques and real-world business decision-making. You will learn how algorithms can uncover hidden patterns, forecast outcomes, and support data-informed decisions that create measurable value. Whether you want to expand your analytical skills or lead data-driven initiatives, this course will help you apply machine learning effectively to achieve meaningful business results.

Students under the old examination regulations (alte Prüfungsordnung (PO) - start before Winter 2024) will receive credit for this course toward the module 'Applied Data Analysis'!

Learning objectives:

• After actively participating in the module, students can describe the fields of application of business analytics and classify basic technologies, methods and concepts.
• They are able to classify and differentiate between basic concepts, functionalities and principles of predictive modeling and, in particular, machine (supervised) learning.
• Building on this, they have a comprehensive understanding of the structure and functioning of predictive models based on machine learning methods.
• Students are able to explain the basic steps for developing a comprehensive domain and data understanding, for the exploration and preprocessing of data, and for the development and evaluation of machine learning models using a systematic approach.
• Furthermore, they have mastered the fundamental methods and principles of a range of machine learning models and can apply these to various practical examples, as well as evaluate, interpret and critically question the results.
• In addition, they are able to implement data analysis approaches and machine learning models using the Python programming language to address real-world problems.

Teaching contents:

Lectures:

• Classification of business analytics concepts and related approaches
• Predictive models and differentiation from explanatory models
• Different types of machine learning
• Supervised learning (classification and regression tasks)
• Procedure models in the area of business analytics (e.g., CRISP-DM)
• Domain and data understanding
• Data preprocessing and feature engineering
• Model training with selected machine learning methods (e.g., linear/logistic regression, decision trees, ensemble methods, artificial neural networks)
• Model evaluation

Exercise based on Python Programming:

• Data exploration and visualization
• Data preprocessing and feature engineering
• Model development and training with selected machine learning methods
• Model evaluation and diagnosis

Course information:

Schedule for the summer term 2026:

Schedule for the winter term 2025/2026:

Further information will be announced during the semester.