Module Intelligent Systems, Computer Science (Master) (ER 5)

Introduction: Examples and discussion of intelligent systems

Stochastic processes and decision theory

Data, features, feature assessment and feature space transformations

Recognition by classification

Attributes by estimation

Behaviour recognition by means of discrete and continuous models

Automatic diagnosis

The matter is presented by means of animated slides and extensive derivations at the blackboard. The presentation is available on the internet. For further study four text books are recommended:

• Pattern classification : a unified view of statistical and neural approaches / Jürgen Schürmann New York [u.a.] : Wiley & Sons, 1996.
• Pattern classification / Richard O. Duda ; Peter E. Hart ; David G. Stork. - 2. ed. New York ; Weinheim [u.a.] : Wiley, 2001.
• Pattern recognition / Sergios Theodoridis and Konstantinos Koutroumbas. - 3. ed. Amsterdam ; Heidelberg[u.a.] : Elsevier Academic Press, 2006.
• Learning with Kernels : support vector machines, regularization, optimization, and beyond / Bernhard Schölkopf ; Alexander J. Smola Cambridge, Mass. [u.a.] : MIT Press, 2002.

Class (including training) (32h), self-responsible work (58h)