Statistical Learning

What it is about

You learn the basics and advanced methods of supervised and unsupervised statistical learning. In the end you will be able to understand and apply models for regression and classification, use techniques for dimensionality reduction and cluster analysis, and follow probabilistic formulations of learning problems while designing new algorithms.

What you will be able to do

• Differentiate supervised vs. unsupervised learning
• Apply high-dimensional regression and shrinkage methods
• Understand and use linear and logistic classification
• Use resampling methods (cross-validation, bootstrap)
• Apply decision trees and random forests
• Perform dimensionality reduction using principal component analysis
• Apply clustering and mixture models
• Understand fundamentals of graphical models
• Apply probabilistic formulations of learning problems
• Design new learning algorithms for new models

What the module consists of

• VorlesungDelivery of content through lectures and discussions, prompting literature study
• ÜbungGuided and increasingly independent work on case studies and problems, partly in small groups

Teaching method

• Vortrag und Diskussionfor the transmission of concepts and further deepening through exchange
• Praktische Übungen und Fallbeispielefor application and acquiring competencies through hands-on work