Contents
Due to advances in data availability and computing power, machine learning (ML) applications are becoming increasingly important for both industry and public institutions. Examples include HR managers using ML predictions to assess the future job performance of applicants during the hiring process, insurance agents using ML to calculate risk premiums of potential customers, banks using ML to assess the default risk of borrowers, and judges relying on algorithmically generated risk assessments to make bail decisions. Against this backdrop, it is increasingly important to understand how modern ML methods work and how they can support decisions.
This course provides an introduction to the workings, opportunities, and risks of using ML applications to support management decisions. Topics covered include data preparation, integration, analysis, Supervised ML, and the importance of ML applications in decision making processes. Students will implement and apply the methods using the Python programming language and associated libraries.
Learning outcomes
After completing this course, you will
Necessary prerequisites
min. 4th semester
Recommended prerequisites
The course serves as an introduction and is therefore accessible to all students (no technical background required). First knowledge in Python is helpful but not necessary.
Forms of teaching and learning | Contact hours | Independent study time |
---|---|---|
Lecture | 2 SWS | 7 SWS |
ECTS credits | 3 |
Graded | yes |
Workload | 90h |
Language | English |
Form of assessment | Written exam (60 min) |
Restricted admission | yes |
Further information | Portal2 |
Examiner Performing lecturer | ![]() | Prof. Dr. Kevin Bauer Prof. Dr. Kevin Bauer |
Frequency of offering | Spring semester |
Duration of module | 1 semester |
Range of application | B.Sc. Bus. Adm., other Bachelor programs (depending on respective study regulations) [9] |
Preliminary course work | – |
Program-specific Competency Goals | CG 1, CG 2 |
Literature | Raschka, Sebastian (2015): Python Machine Learning, Packt Publishing Aurélien Géron (2017): Hands-On Machine Learning with Scikit- Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly |
Course outline | 1. Introduction to Data Science & Machine Learning for business
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