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:	For more information please refer to alma.
Prerequisite for:	---
Prerequisites:	Prior experience with programming in Python ideally but not necessarily through an introductory Python course like DS404 Data Science with Python or DS400 Data Science Project Management.
Course Type:	Lecture (2 weekly lecture hours)
Date:	Lecture on: Thursdays from 10 - 12 am c.t. (Beginning of the first lecture October 17, 2024) - room HS 04 Neue Aula
Registration:	Limited to 28 participants. Registration will open in September on alma. Registration will close on October 13, 2024. If the number of applications exceeds the number of places available (28), we will randomly select from all applications. Preferred access for students from the M.Sc. 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 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: 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.