Statistical Learning 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

1st semester: Students have prior knowledge in mathematics/statistics equivalent to 8 ECTS and are therefore familiar with basic statistical measures as well as fundamental statistical test procedures (e.g., t-test). / 2nd semester: No prerequisites / 2nd semester: Module examination MLAL.A1 (Algorithmics 1)

The following topics are covered in the course:

- Advanced modeling techniques
- Ensemble methods
- Optimization of models

- 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)

Written exam

English

6

The following methods are used:

- Lecture with discussion
- Working on practice exercises
- Interactive workshop

2