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: | Beginning of the first lecture: December 19, 2019 from 10 a.m. c.t. - 2 p.m. - PC Lab, Nauklerstr. 47 (ground floor) |
Registration: | Limited to 28 participants. Registration open for all (no first-come, first-served): Registration will open on Monday, November 4, 2019 at 9 a.m. on ILIAS - end of registration time: Monday, December 9, 2019 (23:55 p.m.). If the number of applications (limited to 28 participants) exceeds the number of places available, we unfortunately will not be able to accept all of the 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), Assignment Regular date: Additional date: |
Content: | Machine Learning Methods have strongly grown in popularity over recevent 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 statistcal 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. |