Module

Voraussetzungen

Inhalte

In the lectures the theory and methods are presented. The knowledge about the material is deepened in self-organised studies and practized in the tutorial at hand of homework problems. Major topics are: • important concepts in linear algebra and algebra • analytic geometry, e.g. projection mappings • linear regression • matrix factorization, e.g. Cholesky decomposition • basics of optimization, e.g. in gradient descent • probability and distributions, e.g. Bayes’ theorem, stochastic gradient descent • dimensionality reduction, e.g. by principal component analysis, singular value decomposition • selected applications in artificial intelligence

Lernziele

After successfully completing the module, students will be acquainted with basic principles and main techniques of mathematical methods that are useful in artificial intelligence and machine learning. They understand the inner workings of the techniques. They are able to apply the presented methods.

Prüfungsleistung

• Written test 1, 30 minutes (weighting: 1/3) • Written test 2, 30 minutes (weighting: 1/3) • Written test 3, 30 minutes (weighting: 1/3)

Bemerkungen

• Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Advanced Methods“