Machine Learning: Unsupervised Methods

This course covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, self-organizing maps, growing neural gas, Bayesian theory and graphical models. We will also briefly discuss reinforcement learning.

Lecturers

Details

Course type
Lectures
Credits
6 CP
Term
Winter Term 2018/2019

Dates

Lecture
Takes place every week on Tuesday from 12:15 to 13:45 in room NAFOF 04/493.
First appointment is on 09.10.2018
Last appointment is on 29.01.2019
Exercise
Takes place every week on Tuesday from 10:30 to 12:00 in room NAFOF 04/493.
First appointment is on 16.10.2018
Last appointment is on 29.01.2019
Preliminary meeting
Takes place every week on Tuesday from 9:00 to 10:30 in room NAFOF 04/493.
First appointment is on 16.10.2018
Last appointment is on 29.01.2019

Requirements

The mathematical level of the course is mixed but generally high. The tutorial is almost entirely mathematical. Mathematics required include calculus (functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities, probability densities, Bayes' theorem, ...).


Goals: (i) The students should get to know a number of unsupervised learning methods. (ii) They should be able to discuss which of the methods might be appropriate for a given data set. (iii) They should understand the mathematics of these methods.

The course is given in English. It will be concluded with an oral exam. The dates will be set in the last lecture.

Literature: For most topics a script will be available.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210