DS405 Machine Learning Applications in Business and Economics
| Lecturer: | Prof. Dr. Stefan Mayer |
| 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 Data Science in Business and Economics |
| Prerequisite for: | --- |
| Prerequisites: | Basic knowledge about R and statistics. |
| Course Type: | Lecture (2 weekly lecture hours) |
| Date: | The lecture consists of (in-person) live sessions and online lecture videos. The dates of the live sessions are:
Live sessions will be in-person, on campus (pending corona regulations). (Beginning of the first lecture: December 16, 2021). |
| Registration: | Limited to 45 participants. Registration open for all (no first-come, first-served): Registration will open on August 17, 2021 on Alma - end of registration time: Friday, November 19, 2021 (23:55pm.). 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 (90 Minutes) or online assessment (90 Minutes). Exam dates are available on the website of the Examinations Office. |
| Content: | Machine Learning Methods have strongly grown in popularity over recent years, and they are now frequently used alongside or even instead of traditional statistical methods. 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 across different fields, and less on the theoretical background. |
| Objectives: | Students can reflect on the proper use of Machine Learning techniques in different subfields of business and economics, they can discuss and evaluate the benefits of different Machine Leaning methods vis-à-vis traditional statistical methods. Students know how to implement these Machine Learning methods in appropriate statistical software (e.g., R), and how to interpret the results. |
| Literature: | Lantz, B. (2015). Machine Learning with R (2nd ed.). Packt Publishing Ltd. |