Computational systems
Introduction and terminology  Evolutionary methods  Montecarlo simulation 
Genetic Algorithms  Gray coding  Parameters coding  Crossover  Mutation 
Selection (by sorting, tournament, elitist)   Optimization and genetic
algorithms – Examples of optimization problems  Schemata  Schemata order and
length  Schemata theorem  Genetic programming  Polish notation  Trees
crossover  Tree mutation  Solving problemd with genetic programming  Parallel
architectures (mesh, hypercube, pyramids) – Examples of parallel algorithms –
Speedup, Overhead.
Textbook: Zbigniew Michalewicz, Genetic Algorithms
+ Data Structure = Evolution Programs, Springer, 1996.
Advanced methods for dataanalysis
Statistical methods  Regression (linear, generalized)  Multivariate
dataanalysis  Statistical Inference  Bayesian models and Networks  Learning
and generalization  Principal Component Analysis  Neural Networks  Decision
trees
Textbook: M.Berthold, D.j.Hand, Intelligent Data Analysis (An introduction)
