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 analysis. Bayesian estimate and comparison with regularization. 

Learning agents.
Different learning modalities: supervised, unsupervised and reinforcement learning. Clustering and metrics. K-means and quad-tree decomposition. Hierarchical clustering. Artificial neural networks. Linear and non-linear perceptrons. Kohonen maps and competitive learning. Multi-scale regression. 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.

BIBLIOGRAPHY

Part I - Symbolic Intelligence and Fuzzy Systems
Lesson 1: Introduction
•	Russell-Norvig, Paragraphs 26.3, 26.4
•	Paul and Patricia Churchland: Può una macchina pensare? Da neuroing.com.
Lesson 2:  Matrixes and Linear Systems. Non-linear optimization. Gradient optimizaiton.
Lesson 3: Fuzzy Systems
·	Kosko, Cap. 7-8. 

Parte II - Reinforcement learning, algoritmi genetici e fuzzy system
Lesson 4: Reinforcement Learning: l’apprendimento degli agenti in setting non associativo.
•	Sutton. Paragrafi: 1.1, 1.2, 1.3, 2.1, 2.2, 2.5, 2.6, 2.7, 2.9, 2.11.
Lesson 5: Learning with reinforce. Markov models and the Value Function
•	Sutton and Barto, Paragrafo 1.4 e Capitolo 3. fino al paragrafo 3.6 (compreso).
Lesson 6: The recursive formulation of the Value function: the Bellman equations
•	Sutton and Barto, paragrafo 3.7.
Lesson 7: Iterative Policy evaluation. Optimization of the policy
•	Sutton and Barto, paragrafi: 3.8, 3.9, 3.10, 4.0, 4.1, 4.2., 4.3, 4.4, 4.5, 4.6, 4.8.
Lesson 8:  Learning by temporal difference, SARSA 
•	Sutton and Barto, paragrafi: 6.0, 6.1, 6.2, 6.4
Lesson 9:  Q-learning and eligibility trace.
•	Sutton and Barto, paragrafi: 6.4, 6.5, 7.0, 7.1, 7.3, 7.5, 7.6. 
Lesson 10:  Fuzzy RL. Stochastic learning automata.
•	Notes.
•	N.A.Borghese, A.Rossini and C.Quadri, Clever Pacman. Proc. WIRN2011.
•	Cattinelli I. and Borghese N.A. Interacting with an artificial partner: modeling the role of emotional aspects, Biological Cybernetics, 2008.

Part III - Biological Intelligence
Lesson 15: The neuron
·	Kandell, Shwartz & Jessel, capitoli: 1, 2 della 4a English edition.
Lesson 16: The central nervous system
·	Kandell, Shwartz & Jessel, capitoli: 19, 20, 38 della 4a edizione inglese. 
Lesson 17: Genetic algorithms and evolutionary optimization
·	Note.
•	Cerveri P., Pedotti A., Borghese N.A., Combined evolution strategies for dynamic calibration of video based measurement systems, IEEE Transaction on Evolutionary Computation, Volume: 5 Issue: 3, pp. 271-282, June 2001. (solamente per la parte di ottimizzazione evoluzionistica). 

Part IV - Machine learning
Lesson 11: Stochastic learning and maximum likelihood
·	Bishop, capitoli: 1.2.3, 1.2.4, 1.2.5, 2.3.4.
Lesson 12: Expectation Maximization and applications
·	Bishop, capitoli: 2.3.9, 9.4. 
	Alternatively: Sean Borman: The Expectation Maximization Algorithm, A short tutorial.
Lesson 13: Bayesian filtering
•	M. Bertero, H. Lanteri, L. Zanni, Iterative image reconstruction: a point of view, Proc. Centro De Giorgi.
Lesson 14: Mixture models
·	Bishop, capitoli: 9.1, 9.2, 9.3.
Lesson 18: learning and lustering
·	Hertz et al., capitoli: 1.0, 1.1.
•	R. Xu and D.C.Wunsch. Survey of Clustering Algorithms. IEEE Transactions on Neural Network, Vol. 16, no. 3, pp. 645-657, 2005. Publihsed in 2008 as abook edited by Wiley, with title: "clustering" (look here Hierarchical Clustering).
Lesson 19: Neural Networks
·	Hertz et al., capitoli: 5.0, 5.1, 5.2, 5.4, 5.5.
Lesson 20: Virtual Reality
·	Note
Lesson 21: Kohonen Maps.
·	Hertz et al., capitolo: 9.0, 9.4.

TEXT USED
Russel Norvig, Artificial Intellgence, a Modern Approach, Prentice Hall 2003. 
Sutton and Barto, Reinforcement Learning - An Introduction, MIT Press, 1998. The book is on line at: http://www.cs.ualberta.ca/~sutton/book/the-book.html.
B. Kosko - Neural Networks and Fuzzy Systems, Prentice Hall, 1991.
C. Bishop. Bayesian Learning. Pattern Recognition and Machine Learning. Springer Verlag.
Kandell, Shwartz & Jessel. Principles of Neural Science. 3rd Edition (in inglese). Elsevier.
Hertz, Krough and Palmer, Introduction to the theory of Neural Computation, Addison Wesley, 1991.