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 
20170925  Administrivia. Introduction to Artificial Intelligence. Intelligence and rationality. Some basic theoretical computer science notions. 


20170928  Agents, percepts and actions. The agent function. Rational agents. Environment types. Examples. Structure of agents. Reflex agents. 

20171002  Problemsolving agents. Examples. Search graphs and search trees. Performance measures. Blind search (depth and breadth first). 

20171005  Analysis of BFS. Depthlimited search and iterative deepening. Uniform cost search. Optimality. Bidirectional search. 

20171009  Heuristics. Greedy best first search. Admissibility and consistency. A*. Optimality of A*. Performance measures. Designing heuristics. Pattern databases. 

20171012  Python practice on blind and heuristic search.  
20171016  Local search. Hill climbing. Beam seach. Simulated annealing. Introduction to constraint programming. Constraint satisfaction problems. Examples. 

20171019  Inference for CSPs (constraint propagation). Node and arc consistency. AC3 and its analysis. Path consistency. Introduction to search for CSPs. 

20171023  Backtracking search. Variable and value ordering. Maintaining arc consistency. 

20171030  Minconflict. Forward checking. Directed arc consistency. Solving tree problems. Cutset conditioning. Dual problems and their networks. Junction trees. 

20171102  Constraint modeling with Minizinc and Numberjack. 

20171106  Logicbased agents. Knowledge bases. Entailment and logical inference. Model checking. Propositional logic. Syntax and semantics. Decidability. Satisfiability. Deduction theorem. Propositional theorem proving. 

20171109  Resolution. Proofs by resolution. Conjunctive normal form. Ground resolution theorem. Definite clauses and Horn clauses. 

20171113  Forward and backward chaining. SAT and the DPLL procedure. Random SAT problems. WalkSAT. 

20171116  Beliefs, probabilities, and probabilistic reasoning. Conditional independence. Examples. 

20171120  Homework: Solve problem in RN10 13.6. Directed graphical models (Bayesian networks). Semantics of directed networks. Conditional independence entailment. Examples. 

20171123  Dseparation and conditional independence in Bayesian networks. Conditional probability tables. 

20171123  Brief introduction to Hugin  
20171127  Inference. Junction trees for probabilistic inference. Algebra of probability tables. Absorption. Local and global consistency. Propagation in junction trees. Introduction to parameter learning in directed graphs. Maximum likelihood estimation for complete data. 

20171130  Machine learning and supervised learning. The Naive Bayes classifier. Training error, true error, using a test set to estimate the true error. 

20171204  Laplace smoothing for Naive Bayes. Multinomial model for text categorization. kfold crossvalidation. Decision trees. Topdown induction algorithm. 

20171207  Splitting criteria for decision trees. Why classification error does not work as measure of impurity. Gini index. Entropy criterion. Combatting overfitting with pre and postpruning. Handling continuous attributes. Rule pruning. Random forests. 

20171214  Perceptron. BlockNovikoff theorem. 

20171214  Introduction to ScikitLearn.  
20171212  Optional Seminar: Intel AI Academy Seminar on Machine Learning and Deep Learning Fundamentals. Room 111 Santa Marta, 14:30  
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