Statistical and Machine Learning (WS 23/24)

The students learn to understand foundational concepts that underpin supervised and unsupervised learning models, as well as the related computation and inference approaches. 

Topics: regularization, tree-based methods, kernel methods, clustering, dimension reduction,  an introduction to neural networks and computational method.