First Semester
Fri: 11:00 a.m. - 1:00 p.m., Room E4
Fri: 2:00 p.m. - 4:00 p.m., Room E2
Introduction [pdf]
Alan Turing (Wikipedia)
Computer chess (Wikipedia)
Shannon, C., "Programming a Computer for Playing Chess", Philosophical Magazine, 41 (314), 1950 [pdf]
Campbell, M., Hoane, A. J., Hsu, F., "Deep Blue", Artificial Intelligence, 134 (1-2), 2001 [pdf]
"Building Watson - A Brief Overview of the DeepQA Project", YouTube, 2011 [video]
"Final Jeopardy! and the Future of Watson", TED, 2011 [video]
Ferrucci, D., et al., "Building Watson: An Overview of the DeepQA Project", AI Magazine, 3 (31), 2010 [pdf]
Formal Logic [pdf]
Language, schemas and reasoning
Syllogism (ancient logic) (Wikipedia)
Propositional Logic [pdf]
Boolean algebras, formal propositional language and its semantics, satisfiability, entailment
Decisions and Algorithms [pdf]
Decision problems, entailment as a satisfiability problem (i.e. refutation), computational complexity, Semantic Tableau
The Halting Problem (Wikipedia)
Big O notation (Wikipedia)
The NP complexity class (Wikipedia)
Propositional Resolution [pdf]
Resolution as inference rule, propositional resolution by refutation
First-Order Logic [pdf]
First-Order semantic structures, language, validity
Semi-decidability of First-Order Logic [pdf]
Prenex normal form, skolemization, Herbrand's theorem
First-Order resolution [pdf]
Clausal form, unification, resolution method for first-order logic
The world of lists in Prolog [pdf]
Re-definition of append/3 using the function cons/2 [pl]
Prolog examples are compatible with SWI-Prolog
(free software) [link]
SLD resolution [pdf]
Horn clauses, SLD resolution, logic programming
Minimal models, logic programs [pdf]
Herbrand models, minimal models, logic programming systems
Probabilitistic reasoning: representation [pdf]
Foundations of probability, random variables, graphical models
Probability space (Wikipedia)
Probability axioms (Wikipedia)
Random variable (Wikipedia)
Probabilitistic reasoning: inference [pdf]
Examples of graphical models, inference, computation methods
Examples with "Bayes" (free software) available on AISpace
[download] (see Belief and Decision Networks)
Probabilitistic reasoning: learning [pdf]
Learning proababilities from observations: maximum likelihood estimator, maximum a posteriori probability,
conjugate prior distribution
Learning with numbers [pdf]
K-means, Expectation-Maximization (EM) algorithm
Ng, Andrew, The EM Algorithm, Stanford Engineering Everywhere (SEE), Course Notes [pdf]
Examples of the K-means (i.e. Lloyd's) algorithm [Java applet]
Examples of the EM algorithm for the mixture of Gaussians model [Java applet]
Reinforcement learning [pdf]
Multi-armed bandits and methods, Thompson sampling, Markov Decision Processes, value function, optimal policy, iterative learning algorithms
Sutton, R.S., Barto, A.G., Reinforcement Learning: An Introduction, 2nd Edition (draft)
[link]
(at this time, the draft is freely available at the above link)
Self-organizing systems [pdf]
Fritzke, B., Some Competitive Learning Methods, Ruhr-Universität Bochum, TR, 1997
[pdf]
Growing Self-Organizing Networks
[Java applet]
Marsland, S., A self-organising network that grows when required, Neural Networks, 15, 2002 [pdf]
Marco Piastra
Contact: marco.piastra@unipv.it
AI Question Time
Mon 2014.01.27 Aula E4 3pm-5pm
Mordechai Ben-Ari. Mathematical Logic for Computer Science (3rd Edition). Springer, 2012
Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall, 2009.