Marketing

DS405 Machine Learning Applications in Business and Economics

Lecturer: Dr. Aseem Behl
Course description:

DS405

Language: English
Recommended for this semester or higher: 1
ECTS-Credits: 6
Course can be taken as part of following programs/modules:

Economics and Finance
European Management
General Management
International Business
International Economics
Management and Economics

Data Science in Business and Economics

Prerequisite for:

---

Prerequisites:

Prior experience with programming in Python ideally through an introductory Python course like DS404 Data Science with Python.

Course Type: Lecture (2 weekly lecture hours)
Date:

Lecture on: Thursdays from 10 - 12 am c.t. (Beginning of the first lecture October 20, 2022) - room HS 05 Neue Aula

Registration:

Limited to 45 participants. Registration open for all (no first-come, first-served): Registration will open on September 1, 2022 on Alma - end of registration time: Sunday, October 9, 2022. If the number of applications (limited to 45 participants) exceeds the number of places available, we unfortunately will not be able to accept all applicants. In this case, a random selection will be made from all the applications received. Preferred access for master students from the Data Science in Business and Economics program.

Downloads: ILIAS
Method of Assessment: Written Exam or Presentation or Assignments or Term Paper.
Examination Timetable will be available on the website of the Examinations Office.
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, 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:
1. Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka , Yuxi (Hayden) Liu , Vahid Mirjalili
2. Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy.
3. Patterns, Predictions, and Actions. A story about machine learning. Moritz Hardt and Benjamin Recht.