You will learn about the basic concepts and the main research areas of AI. Broad topics that are covered include: searching, constraint programming, logic, probabilistic reasoning, and machine learning.
B003368 (Algorithms and Data Structures)
[RN10] Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd edition. Pearson, 2010.
[B12] D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.
[HTF09] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. 2nd edition. Springer, 2009.
[STC00] John ShaweTaylor and Nello Cristianini. Support Vector Machines and other kernelbased learning methods, Cambridge University Press, 2000
[PM10] David L. Poole, Alan K. Mackworth. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press, 2010.
[D03] Rina Dechter. Constraint Processing. Morgan Kaufmann, 2003.
[F94] P. Flach. Simply Logical. Intelligent Resoning by Esample. Wiley 1994.
[GN87] M. Genesereth, N. J. Nilsson. Logical Foundations of Artificial Intelligence. Morgan Kaufmann Publishers, 1987. Alibris
There is a final project and a final oral exam. The final project will be assigned by the teacher on one of the main topics of the course (Learning, Searching, Constraints, Logic programming). The oral exam covers the rest of the course.
Date  Topics  Readings/Handouts 
20160927  Administrivia. Introduction to Artificial Intelligence. Intelligence and rationality. Some basic theoretical computer science notions. 


20160929  Agents, percepts and actions. The agent function. Environment types. Examples: Structure of agents. Reflex agents. Goaldriven agents. 

20161004 
Problemsolving agents. Examples. Search graphs and search trees. Performance measures. Blind search (Depth and breadth first).
Homework: Write a general pseudocode for blind search that works for both BFS and DFS. Try variants with and without a closedlist and with goal test at expansion or at pop from the open list. Analyze running time and space. 

20161006  More blind search (uniform cost search, depthlimited search, iterative deepening, bidirectional search). 

20161011  Heuristics. Greedy best first search. Admissibility and consistency. A*. Optimality of A*. Performance measures. Local search, hill climbing, simulated annealing. 

20161013  Python practice on blind and heuristic search.  
20161018  Constraint satisfaction problems. Examples. Node and arc consistency. AC3. 

20161020  Backtracking search. Variable and value ordering. Maintaining arc consistency. Forward checking. Examples. 

20161025  Minconflict. Path consistency and Kconsistency. Directed arc consistency. Solving tree problems. Cutset conditioning. Junction trees. 

20161027  Constraing modeling with Minizinc and Numberjack.  
20161103  Logicbased agents. Knowledge bases. Entailment and logical inference. Model checking. 

20161108  Propositional logic. Syntax and semantics. Decidability. Satisfiability. Deduction theorem. Propositional theorem proving. 

20161110  Resolution. Proofs by resolution. Conjunctive normal form. Ground resolution theorem. Definite clauses and Horn clauses. 

20161115  Forward chaining. SAT and the DPLL procedure. Random SAT problems. WalkSAT. First order logic. Syntax and semantics. 

20161117  Inference in 1st order logic. Universal and existential elimination. Skolemization. Propositionalization. Unification. Theorem proving using generalized modus ponens. Resolution in FOL. 

20161122  Beliefs, probabilities, and probabilistic reasoning. Conditional independence. Examples. 

20161124  Directed graphical models (Bayesian networks). Semantics of directed networks (dseparation). Conditional independence entailment. Examples. 

20161129  Dseparation and conditional independence in Bayesian networks. Inference. Message passing in linear chains. Belief propagation. Junction trees. 

20161201  Algebra of probability tables. Construction of junction trees for directed networks. Propagation in junction trees. 

20161126  Maximum likelihood learning for Bernoulli and categorical distributions. Learning Bayesian networks with maximum likelihood (complete data). Introduction to Bayesian learning. Beta distribution. Bayesian conjugacy. Bayesian learning for Bernoulli. 

20161213  Dirichlet priors. Bayesian learning of belief nets (complete data). Structure learning. Supervised learning. The Naive Bayes classifier. Bernoulli model. Laplace smoothing. Estimating the prediction accuracy of a classifier. Learning curves. 

20161215  Induction of decision trees. Topdown algorithm. Splitting criteria. Why misclassification error does not work. Gini index. Entropy criterion. Pre and postpruning. Rule pruning. 

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