course: Machine Learning: Unsupervised Methods

number:
310003
teaching methods:
lecture with tutorials
responsible person:
Prof. Dr. Laurenz Wiskott
lecturer:
Prof. Dr. Laurenz Wiskott (Neuroinformatik)
language:
english
HWS:
4
CP:
6
offered in:
winter term

dates

Please look up the dates in the central course catalog.

Exam

Oral

Date according to prior agreement with lecturer.

Duration: 30min
Exam registration: FlexNow

goals

After visiting this course students have knowledge in several methods of machine learning, i.e.: principal component analysis, clustering, vector quantization, self-organizing maps, independent component analysis, Bayesian theory and graphical models, linear regression, backpropagation of error, generalization and support vector machines.

content

This course covers a variety of methods from machine learning such as principal component analysis, clustering, vector quantization, self-organizing maps, independent component analysis, Bayesian theory and graphical models, linear regression, backpropagation of error, generalization and support vector machines.

requirements

none

recommended knowledge

Good command of linear algebra and calculus.