Modulbeschreibung

The goal is to enable the students to understand the fundamental statistical machine learning algorithms for diverse datasets. To this end, the theory of these algorithms is developed in the lectures and during the practice sessions, many such data sets are analyzed.

• Since this module is taught in English, a reasonable command of the English language is required
• Fundamental knowledge of linear algebra, calculus and probability theory is required to be able to follow the lectures
• The book is available online for free: http://www-bcf.usc.edu/~gareth/ISL/
• Please browse through the book to get an understanding of the complexity of the material covered in this module

Knowledge of the statistical machine learning approaches for diverse data sets and the ability to apply the appropriate algorithm for successfully solving a given machine learning problem.

• Linear regression
• Classification
• Resampling Methods
• Linear Model Selection and Regularization
• Moving Beyond Linearity
• Tree-Based Methods
• Support Vector Machines
• Unsupervised Learning

The module is based on the excellent and freely available book: "An Introduction to Statistical Learning" from Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.