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, Graphical Models). The topic will be decided discussing with the student. The project typically consists of modeling or application of AI techniques to simple real problems, or it could involve the implementation and the verification of algorithmic techniques described in the course. The project will be discussed during the oral examination.
The oral exam covers the rest of the course. During the exam, students should prove they can master both theoretically and practically the methods and the algorithmic techniques described in the course. 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.
Tuesday, 10:4512:45
Please do not email me about office hours, just check the School of Engineering Website for (unlikely) changes
Date  Topics  Readings/Handouts 
20180924  Administrivia. Introduction to Artificial Intelligence. Intelligence and rationality. Some basic theoretical computer science notions. 


20180927  Agents, percepts and actions. The agent function. Rational agents. Environment types (discrete, static, deterministic, etc.). Examples. Structure of agents. Reflex agents. 

20181001  No class today  
20181004  No class today  
20181008  Searching. Formulation of a search problem. Examples. Search graphs and search trees. Depth and breadth first search algorithms. Completeness and optimality. Time and space analysis. 

20181011  Depthlimited search and iterative deepening. Uniform cost search. Optimality. 

20181015  Bidirectional search. Heuristics. Greedy best first search. Admissibility and consistency. A*. Optimality of A*. Performance measures. Designing heuristics. Pattern databases. Local search. Hill climbing. Beam seach. Simulated annealing. 

20181018  Python practice on blind and heuristic search.  
20181022  Introduction to constraint programming. Constraint satisfaction problems. Examples. Inference for CSPs (constraint propagation). Node and arc consistency. AC3 and its analysis. 

20181025  Limitations of arcconsistency. Path and kconsistency. Backtracking search. Variable and value ordering. Maintaining arc consistency. Forward checking. 

20181029  Directed arc consistency. Solving tree problems. Cutset conditioning. Dual problems and their networks. Junction trees. 

20181101  No class today  
20181105  Constraint modeling with Minizinc and Numberjack. 

20181105  Logicbased agents. Knowledge bases. Formulae, syntax, semantics. Entailment and logical inference. 

20181108  Propositional logic. Syntax and semantics. Decidability. Satisfiability. Deduction theorem. Propositional theorem proving. clauses. 

20181112  Resolution. Proofs by resolution. Conjunctive normal form. Ground resolution theorem. Definite clauses and Horn clauses. Forward and backward chaining. 

20181113  SAT and the DPLL procedure. Random SAT problems. WalkSAT. Syntax and semantics of first order logic. 

20181119  Examples of sentences/theories in FOL. Inference in FOL. Universal and existential elimination. Skolemization. Propositionalization. Unification. Resolution in FOL. 

20181122  Beliefs, probabilities, and probabilistic reasoning. Examples. 

20181126  Probabilistic reasoning. Conditional independence. Examples. Directed graphical models (Bayesian networks). Semantics of directed networks. Examples. 

20181129  Conditional independence entailment. Dseparation and conditional independence in directed networks. 

20181203  Brief introduction to Hugin  
20181203  Inference. Junction trees for probabilistic inference. Algebra of probability tables. Absorption. Local and global consistency. Propagation in junction trees. Construction of junction trees for directed graphical models 

20181206  Introduction to machine learning. Supervised learning. Hypothesis spaces. Training error (empirical risk), true error (risk), estimation the true error using a test set or using crossvalidation. The Naive Bayes classifier. 

20181210  Laplace smoothing for Naive Bayes. Multinomial model for text categorization. Decision trees (definition and their hypothesis space) 

20181217  Top down induction of decision trees. Splitting criteria (Gini, entropy, why classification error does not work as measure of impurity). Handling continuous attributes. Combatting overfitting with pre, post, and rulepruning. Perceptron. BlockNovikoff theorem. 

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