Intelligent System. Master Program in Computer Science. Syllabus for A.Y. 2009-2010. 

Introduction. The Turing machine and the Searle Chinese room. Decision trees. Linear systems and linear estimate. 

Reinforcement learning. Learning in a non associative setting, greedy and epsilon-greedy. Markov models and Value Function. Bellman equations and recursive formulation. Iterative policy evaluation. Policy improvement. Temporal differences. Sarsa and Q-learning. The eligibility trace. Stocastic learning automata and reinforcement learning. 

Biological intelligence
The neuron and the central nervous system. Processing inside the cortex. Language and connectionist models. Genetic algorithms. Fuzzy set and fuzzy systems.

Statistical learning. Supervised and unsupervised learning. Clustering. K-means, quad-tree decomposition and hierarchical clustering. Neural networks and supervised learning. Kohonen maps. Introduction to Bayesian statistics. Maximum likelihood and least squares estimate. Expectation maximization and mixture models. Bayesian filtering and regularization theory. Applications.