Table of Contents

1. Introduction to Supervised and Unsupervised Learning
2. Statistical Learning Theory
3. Linear Regression
4. Classification
5. Resampling Methods
6. Linear Model Selection and Regularization
7. Tree-based Methods
8. Support Vector Machines
9. Unsupervised Learning Methods

In addition, the course will be complemented by discussions on selected topics of Data Science and Machine Learning, e.g., Structuring Data Projects in R, Programming in the Tidyverse, Feature Engineering, Imbalanced Learning, Causal Machine Learning, Machine Learning Workflows in R with Tidymodels.

The seminar will be entirely digital.

Kurs im HIS-LSF