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
Prof. Dr. Laurenz WiskottLecturer |
(+49) 234-32-27997 laurenz.wiskott@ini.rub.de NB 3/29 |
Details
- Course type
- Lectures
- Credits
- 6 CP
- Term
- Winter Term 2016/2017
Dates
- Lecture
-
Takes place
every week on Tuesday from 12.15 to 13.45 in room NB 3/57.
First appointment is on 18.10.2016
Last appointment is on 07.02.2017 - Exercise
-
Takes place
every week on Tuesday from 9.00 to 12.00 in room NB 3/57.
First appointment is on 25.10.2016
Last appointment is on 07.02.2017
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 interdisciplinary research unit of the Ruhr-Universität Bochum. We aim to understand fundamental principles that characterize how organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by insights into 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, theoretical approaches from physics, mathematics, and computer science, including, in particular, machine learning, artificial intelligence, autonomous robotics, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210