Statistical Learning 1 Lab

Master's program

The participants:
• can practically follow the workings of basic data science algorithms
• can configure basic data science algorithms for specific purposes
• can apply the algorithms covered to isolated problems

Students have prior knowledge in mathematics/statistics amounting to 8 ECTS and are therefore familiar with simple statistical measures as well as basic statistical test procedures (e.g. t-test).

In the lab, the content of the integrated lecture "Statistical Learning 1" is deepened through practical exercises. The insights gained are discussed in the group and thus allow a deep understanding of the subject matter and a consolidation of the knowledge that was 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

German

2.5

The following methods are used:

- Working through practice exercises
- Interactive workshop

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