Oral or written exam depending on number of participants; 60% exercise points as pre-requisite; a limited amount of exercise points may count as bonus points in the exam.

This lecture provides an introduction into statistical machine learning with a
focus on practical application in biomedical data analysis. It comprises basic
methods for supervised (classification, regression) and unsupervised learning.
Topics include but are not limited to:
• Linear models for regression and classification
• Model selection & regularization
• Resampling methods: Cross validation & Bootstrap
• Decision trees
• Ensemble methods: Random Forest & Boosting
• Support vector machines
• Dimensionality reduction
• Clustering algorithms

The students are capable of explaining the most important terms, problems,
and algorithms in statistical machine learning. They are able to decide which
type of methods is promising for analyzing a particular biomedical data set and
to justify that decision by theoretical arguments and empirical studies.

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer Texts in Statistics.

Further books will be announced in the first lecture.