Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

Seminar Machine Learning (SE)


Course Description

The aim of machine learning is to develop methods that are able to automatically detect pattern in data and to use these to predict future outcomes of interest. Hence, machine learning and statistics are closely related fields, yet differing in terminology and emphasis. This seminar aims to give an introduction to the field with basic concepts and algorithms as well as examples drawn from different application domains. Topics include, amongst others, classification, boosting, graphical models, approximate inference, neural networks and deep learning.

Registration in the respective Moodle course is obligatory.


Participants should be familiar with basic concepts of probability, multivariate calculus, linear algebra and computer programming.

Also, PhD students with related fields of research are welcome.

Course Learning Objectives

Broad overview on topics of machine learning.

Course Structure

Block seminar.

Literature and Sources (source codes)