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. Non-linear Regression Techniques
   
8. Tree-based Methods
9. Support Vector Machines
10. Deep Learning
    
11. Unsupervised Learning Methods

In addition, the course will be complemented by learning videos on selected technical topics of statistical programming, such as "Programming in the Tidyverse", "Dynamic Documents with Rmarkdown", "Version Control Using Git and GitHub" and "Machine Learning Workflows with Tidymodels".

The seminar is planned to be held in presence.

Kurs im HIS-LSF