Computational Intelligence Techniques for Machine Learning

Lecturer:

Robert Babuška (gar.)

Tutor:

Robert Babuška (gar.)

Supervisor:

Department of Cybernetics

Synopsis:

Learning objective:become familiar with the theory and applications of computational intelligence methods in the context of systems capable of learning from data. Introduction, motivation for learning, computational intelligence. Supervised, unsupervised and reinforcement learning paradigms. Fuzzy systems, neural networks, neuro-fuzzy systems, and other general function approximators for supervised learning. Fuzzy clustering methods for unsupervised learning. Reinforcement learning for single-agent and multi-agent systems. Examples of applications and case studies. The course will be connected with - a computer assignment with Matlab/Simulink and a literature assignment.

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans:

• Doctoral studies, daily studies (compulsory elective course)
• Doctoral studies, combined studies (compulsory elective course)