Course Info
| Module [Module Number] | CM Data Analytics II [1277MBMLA1] |
| Regular Cycle | Winter Term |
| Teaching Form | Lecture |
| Examination Form | Written test: Portfolio |
| Teaching Language | English |
| ECTS | 6 |
| Instructor | Prof. Dr. Markus Weinmann, Christopher Coors, Rainer Dyckerhoff |
| KLIPS 2.0 | Link |
Module Content
- Basics of the methods of Machine Learning and Artificial Intelligence (AI)
- Basics of both supervised and unsupervised methods (e.g. decision trees, random forests, boost- ing, support vector machines, neural networks, deep and opponent learning, ensemble learning, principal component analysis, factor analysis and diverse learning or multidimensional scaling)
- Translation of business problems into machine learning use cases; feasibility and impact
- Responsible implementation of machine learning projects in compliance with ethical standards
Learning Objectives
Students …
- know and understand the relevant methods and theories for the points mentioned above under „Module content“.
- understand advanced, specialized theories / methods in the field of machine learning and AI.
- analyse current questions and challenges in the field of machine learning and AI.
- assess and discuss findings and research results of specialized theories / methods.
- act responsibly considering ecological, social and ethical criteria.
- develop work processes for real problems and challenges.