Visualization techniques, principal component analysis, MDS and t-SNE. Hierarchical clustering, k-means clustering.

Specialized methods of multivariate statistics: Classification, tree-based models, support vector machines, neural networks.

The course deals with simple quantitative and graphical as well as more complex methods of biostatistics. Contents: Descriptive statistics, testing hypotheses, confidence intervals, correlation, simple and multiple linear regression, classification and prediction, diagnostic tests, measurement of agreement, causality versus association.

This course introduces probabilistic deep learning (DL). DL is used for data with complex features like images. We treat DL as probabilistic models, as a continuation of GLMs (logistic regression, ...). The models are fitted with maximum likelihood or Bayesian learning.

Extension of statistics for medical students. This lecture is based on the content of Statistics I. The focus will be on the understanding and the concrete application of statistical methods, as they are used in medical research. Exercises will be solved using the statistical programming environment R.