Prerequisites

Statistical and mathematical basics, linear algebra, algorithmic basics

Course Content

The course covers advanced topics of machine learning as for example: Graphical Models in Machine Learning, Inference Mechanisms and Sampling Strategies in graphical models, Gaussian Processes, Probabilistic Topic Models, Unsupervised and Semi-Supervised Learning.

Objectives

Deepening of machine learning knowledge in statistical methods