DS405 Machine Learning Applications in Business and Economics
| Lecturer: | Dr. Aseem Behl |
| Course code: | DS405 |
| Language: | English |
| Recommended for this semester or higher: | 1 |
| ECTS-credits: | 6 |
| Course can be taken as part of following programs/modules: | See alma |
| Prerequisites: | Prior experience with programming in Python is ideal but not necessary, e.g., through an introductory Python course like DS400 Data Science Project Management |
| Limited attendance: | 28 |
| Course type: | Lecture |
| Date: | Thursdays, 10 a.m. - 12 p.m. c.t., room S227 (Hölderlinstraße/Sigwartstraße) First lecture on October 16, 2025 |
| Registration: | By October 19 via alma
If the number of applications exceeds the number of places available, a random selection will be made from all the applications received. Preferred access for students from the M.Sc. Data Science in Business and Economics program. |
| Method of assessment: | Written exam, presentation, assignments |
| Content: | Machine Learning methods have become widely popular in the recent years with many successful commercial use cases such as targeted advertising and digital content recommendation. This module will therefore consider selected Machine Learning methods and study their use and applicability across different fields of business and economics. The focus of this module will be on the application of these methods through case studies, and less on the theoretical background. |
| Objectives: | Students can reflect on the proper use of Machine Learning techniques, they can evaluate the pros and cons of employing several Machine Leaning methods in different contexts emerging in business and economics applications. Students can implement these Machine Learning methods in Python with the help of Machine Learning frameworks. |
| Literature: | There is no required textbook for this module. Some lectures may recommend readings from the following books:
|
| Downloads: | --- |
| ILIAS: | TBD |