Neural computers and their applications

Lecturer:

František Hakl (gar.), Martin Holeňa (gar.)

Tutor:

František Hakl (gar.), Martin Holeňa (gar.)

Supervisor:

Department of Mathematics

Synopsis:

Introduction into the theory of artificial neural networks, some important kinds of neural networks, threshold vectors analysis of binary nets, neural networks evaluation of Boolean functions, neural networks from the point of view of function approximation, neural networks from the point of view of probability theory, numerical properties of learning algorithms.

Requirements:

Syllabus of lectures:

Introduction to neural networks, basic models, analysis of binary neural networks, approximations possibilities of neural networks, Vapnik-Chervonenkis dimension of NN, learning theory and neural networks, numerical aspects of learning algorithms, applications of probabilistic theory in neural networks, fuzzy sets approach to neural networks.

Syllabus of tutorials:

Study Objective:

Make the students acquainted with theoretical and mathematical fundamentals of important kinds of neural networks.

Study materials:

[1] H. White. Artificial Neural Networks: Approximation and Learning Theory. Blackwell Publishers, Cambridge, 1992 [2] Vwani Roychowdhury, Kai-Yeung Siu, Alon Orlitsky. Theoretical Advances in Neural Computation and Learning. Kluwer Academic Publishers, 1994

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: