Machine Learning and Decision Making
General Remarks
The course will be delivered in a blocked format over six weeks with up to three sessions per week, in the period from May 18 to June 26, 2026.
Lecture times: Thu 8:00-12:00 in HeHo 18 E.20 & Fri 8:00-10:00 in HeHo 18 E.20.
For organizational questions, please contact Stefan Rausch.
For further information, please refer to Moodle.
Characterizing the course
The course aims to develop your analytical skills - both the ability to conceptualize problems as well as solve them broadly. It aims to make you understand and appreciate the most widely used tools of machine learning which form the basis for rational and sound decisions. The main topics to be covered are:
- Problem recognition and hypothesis/model testing in the context of managerial decision-making.
- Developing skills in the analysis and interpretation of data.
- Handling challenging problems using appropriate analysis tools.
Literature
- Gareth, J., D. Witten, T. Hastie and R. Tibshirani, “An Introduction to Statistical Learning with Applications in R”, Springer series.
- Additional references will be provided in class.