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 in winter term

  • start: Tuesday the 09.10.2018
  • lecture with integrated tutorials Tuesdays: from 12:15 to 13.45 o'clock in NB 3/57

Exam

Date according to prior agreement with lecturer.

Form of exam:oral
Registration for exam:FlexNow
Date:None
Duration:30min

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.