Software Engineering and Machine Learning

General Information

This course takes place yearly in the winter term.

• Master
• Scope: 2 SWS lecture + 2 SWS exercise
• Credits: 5
• Language: English
• Abbreviation: SEML
• Exam: Written exam
• Course number: 

Contents

• Understanding the interactions between software engineering and machine learning

• Understanding the risks and chances of Machine Learning in software systems

• Using Machine Learning for Software Engineering

• Using Software Engineering for Machine Learnin

Learning Outcomes and Competences (Excerpt from the Module Handbook)

Students should understand the consequences of deciding to use machine learning. They should be enabled to select suitable methods from both areas and learn to assess their opportunities and risks.

Recommended Proficiencies

Basic knowledge in Software Engineering and Machine Learning

Literature

• Ian Sommerville: Software Engineering. 10. Aufl. Pearson, 2018, ISBN 978-3-86894-344-3.

• Haykin, Simon: Neural networks and learning machines, Prentice Hall, 2008

• Molnar, C. (2025). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (3rd ed.)

• Kalech, Meir and Abreu, Rui and Last, Mark. Artificial Intelligence Methods for Software Engineering, WORLD SCIENTIFIC, 2021

• In der Lehrveranstaltung wird es weitere Hinweise zu Literatur geben, da es ein sich rasant entwickelndes Forschungs- und Anwendungsfeld ist. Teilweise wird auch auf Primärliteratur zurückgegriffen

Winter term 2026

• Lecturers: Thomas Thüm, Raphael Dunkel
• Further information will follow shortly