Statistical Learning 1

Master's degree program

The participants:

• can describe how basic algorithms in the field of data science work
• can understand the statistical concepts and approaches behind the algorithms covered
• can select suitable algorithms for given problems
• can identify the data structures, runtime characteristics, and complexity classes required by the algorithms covered
• can apply the algorithms covered to isolated problems

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).

The following topics are covered in the course:

- Statistical measures (point and interval estimators)
- Statistical test procedures
- Clustering algorithms (classification trees, agglomerative hierarchical clustering, etc.)
- Regression algorithms (regression trees, random forests, etc.)
- Association algorithms
- Data preprocessing techniques (e.g., principal component analysis)

- 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

1