Lectures

8.3072 Machine Learning (Lecture & Practice) (auch KOGW-WPM-NI)

Heidemann, Krumnack

Type Language Semester Credits Hours Room Time Term Year
V e 4 12 6 Mi 10-12, Do 10-12, Di 14-16 S 2017
BSc: Neuroinformatics (KOGW-PM-NI)
BSc examination field: Neuroinformatics (KOGW-WPM-NI)
BSc examination field: Computer Science (KOGW-WPM-INF)

Syllabus:

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, artificial neural networks (e.g. multilayer perceptrons, RBF networks, self-organizing maps), classification methods, reinforcement learning, modeling uncertainty and temporal probability models.

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