Statistical Learning Lab 2

Master's degree program

The participants:

• can practically follow advanced data science algorithms
• can configure advanced data science algorithms for specific purposes
• can apply the algorithms covered to isolated problems

Module examination MLAL.A1 (Algorithmics 1)

In the lab, the content of the integrated lecture "Statistical Learning 2" is deepened through practical exercises. The insights gained are discussed as a group, thereby providing a thorough understanding of the subject matter and consolidating the knowledge conveyed theoretically in the integrated lecture.

- James, G.; Witten, D; Hastie, T.; Tibshirani, R. (2023). An Introduction to Statistical Learning: with Applications with Python (ISBN: 978-3031387463)
- Bruce. P.; Bruce, A.; Gedeck, P. (2020): Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (ISBN: 978-1492072942)
- Geron, A. (2022): Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, (ISBN: 978-1098125974)

Term paper
Project

English

2.5

The following methods are used:

- Working on practice exercises
- Interactive workshop

2