Intelligent Systems. Master Program in Computer Science. Syllabus.

Symbolic Intelligence
The Turing machine and the Chinese room. Weak and Strong position in Artificial Intelligence Collective intelligence. The fuzzy set and the fuzzy systems.

Machine learning
Statistical learning: Statistical distributions. Maximum likelihood and least squares estimate. Variance on the estimated quantities. Bayesian estimate and the comparison with regularization. Expectation Maximization. Mixture models. Applications.
The different learning modalities: supervised, unsupervised and reinforcement learning. Clustering and evaluation metrics. K-means and quad-tree decomposition. Hierarchical clustering. The artificial neural networks. Linear and non-linear perceptrons. Kohonen maps and competitive learning. Applications.

Reinforcement learning
Learning in a non-associative setting. Stationary and non-stationary case. Greedy and e-greedy techniques. Learning in an associative setting. The Markovian models. Computation of the Value Function. Recursive formulation of the computation of the Value function and Bellman's equations. Policy improvement through iterative algorithms based on the Value function. Learning from Temporal Differences. Q-learning. Increasing the temporal span through the eligibility trace. Incorporating a-priori information: Fuzzy Reinforcement learning and stochastic learning automata.

Biological intelligence. 
The neuron. Under-threshold operation. Generation of the action potential. The neuron structure. The biological neural circuits and the role of the structure inside the circuit. Structure of the central nervous system. Language. The cortical areas and the spinal cord. Visuo-motor transformations. Population code as an example of cortical processing. Genetic algorithms and evolutionary optimization. Parameters role. Examples.

Virtual Reality
Input systems, graphical engines, world generators and output systems. Applications.