Modulbeschreibung
Statistical Machine Learning
Kurzzeichen:
Unterrichtssprache:
ECTS-Credits:
Leitidee:
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.
Modulverantwortung:
Standort (angeboten):
Zusätzliche Eingangskompetenzen:
- Since this module is taught in English, a reasonable command of the English language is required
- Fundamental knowledge of linear algebra, multivariable calculus and probability theory is required to be able to follow the lectures
- The practical exercises are with Python or Matlab, hence some familiarity with one of these scripting languages is helpful but not required
- The book is available online for free: https://www.statlearning.com/ An Introduction to Statistical Learning (statlearning.com)
- Please browse through the book to get an understanding of the complexity of the material covered in this module
Modultyp:
Modulbewertung:
Leistungsnachweise und deren Gewichtung
Modulschlussprüfung:
Inhalte
Angestrebte Lernergebnisse (Abschlusskompetenzen):
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.
Modul- und Lerninhalt:
- 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" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. This book has also a useful website https://www.statlearning.com/ An Introduction to Statistical Learning (statlearning.com)