Please note the following credit values:

BSc Course: 4.5 ECTS*

BSc Seminar Course: 9 ECTS

MSc Course: 5 ECTS

MBA Course: 3 ECTS

MBA Workshop: 1 ECTS

Language course: 5 ECTS

*The following BSc courses have a different credit value:

Business Communication: Theory & Practice: 3 ECTS

Managing your personal performance holistically: 3 ECTS

Harmonizing Leadership with Personal Development: 3 ECTS

Mental Health First Aid: 1,5 ECTS

Understanding your personal performance base: 1,5 ECTS

Workshop Body Language for Women: 1,5 ECTS

Intercultural Competence - Fit for International Collaboration: 1,5 ECTS

Perform Yourself! Media and Presentation Coaching: Personal Presence!: 1,5 ECTS

Data Analytics

Participation Prerequisites

none

Course Content

Part 01: Supervised learning The following methods will be introduced and implemented in R with applications to real data: Linear regression, Penalized regression, Logistic regression, CART, Random forests, Boosting, Support vector machines, Artificial neural networks Part 02: Unsupervised learning The following methods will be introduced and implemented in R with applications to real data: Principal Component Analysis (PCA), K-means clustering, Hierarchical clustering Part 03: Visualization A variety of chart types to visualize data for the purpose of data exploration and result communication will be discussed. Part 04: Current limits of Machine Learning and ethical considerations While AI and machine learning have made significant progress over the past years, limitations persist. And new problems such as biases and unauthorized data usage emerge which call for an ethical framework.

Intended Learning Outcomes and Competencies

Foundational knowledge in R Overview of modern machine learning methods Limits of machine learning and artificial intelligence

Instruction Type

hybrid / in class and online participation

Form of Examination

The final project will combine some of the introduced methods on a dataset of the participant’s choice. In case no suitable dataset is available, an alternative dataset will be assigned. The final assignment can be done individually or in teams.

Literature

The following book is a good starting point, further literature will be provided on moodle. T. Hastie, R. Tibshirani, J. Friedman: The elements of statistical learning. Springer, 2009.

Next events

1/3	Lecture	Tu, 26.05.2026	09:00 Uhr	17:00 Uhr	IP-C-101 Hörsaal / Lecture Hall
2/3	Lecture	Th, 28.05.2026	09:00 Uhr	17:00 Uhr	IP-C-101 Hörsaal / Lecture Hall
3/3	Lecture	Fr, 29.05.2026	09:00 Uhr	17:00 Uhr	IP-C-101 Hörsaal / Lecture Hall

Lecturers

Spinler, Stefan

Lecturer

Indicative Student Workload

Self-Study	64 h
Contact Time	24 h
Examination	2 h