Certificate Course

Neural Networks, Genetic Algorithm, Gene Expression Programming and Optimization

Text Book

The Nonlinear Workbook
Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Wavelets, Fuzzy Logic
with C++, Java and SymbolicC++ Programs, second edition
Willi-Hans Steeb
World Scientific, Singapore 2008
ISBN 981 281 852 9

Classical and Quantum Computing
with C++ and Java simulations
Yorick Hardy and Willi-Hans Steeb
Birkhauser, Basel 2002
ISBN 3764366109

The course can be done in self-study.

Artificial neural networks model the functioning of biological neural networks like the brain. They are currently extensively used for pattern recognition (e.g. signature identification on cheques), financial forecasting, control and medical diagnosis. Neural networks are simple models for computing using massive parallelism. This course gives a thorough background on supervised and unsupervised learning in neural networks.
Genetic algorithms are derived from our current understanding of genetic evolution in nature. They are widely used for finding the global minimum of a cost or objective function (especially when many local minima are present). Genetic algorithms can even be used to write computer programs. The algorithms are generally simple and find near optimimum solutions very quickly.

This course is ideally suited for engineers (especially from the fields pattern recognition and control), people interested in stock-market forecasting, medical professionals and computer scientists.

Neural Networks

Genetic Algorithms

Genetic Programming Gene Expression Programming

Timetable and Enrollment Forms.

For further information contact Prof. W.-H. Steeb