Course details


Machine Learning

L + P
SS 2020 Prof. Dr.-Ing. Gunther Heidemann, Ulf Krumnack, Axel Schaffland HYBRID
B.Sc modules:
CS-BP-NI - Neuroinformatics
CS-BWP-AI - Artificial Intelligence
CS-BWP-INF - Informatics
CS-BWP-NI - Neuroinformatics
KOGW-PM-NI - Neuroinformatics
KOGW-WPM-INF - Computer Science
KOGW-WPM-KI - Artificial Intelligence
KOGW-WPM-NI - Neuroinformatics

CS-MW - Master elective course
Tue: 14-16
Wed: 10-12
Thu: 10-12

Being a mainly academic topic about 20 years ago, Machine Learning has become a discipline of major impact on both science and engineering by today. This course introduces the basics of Machine Learning and Data Mining. Major topics are concept learning, decision trees, problems of data in high dimensional representations, clustering algorithms, linear and nonlinear dimension reduction, basic artificial neural networks (e.g., multilayer perceptrons, RBF networks, self-organizing maps), classification methods, reinforcement learning, modeling uncertainty, and temporal probability models.