CS 371  Introduction to Artificial Intelligence
Course Syllabus 
Class  Month  Day  Topic  Readings (parenthesized reading are optional) 
1  January  24  Introduction, "What is AI?"; Uninformed search: problem definition  Course Information page; 4th Hour Requirements: ISLR Introduction, Week 1; (Skim R&N Ch. 1, 2); R&N 3.13.3; Deep Blue article 
2  26  Uninformed Search: breadthfirst search, depthfirst search, recursive depthfirst search, depthlimited search, iterativedeepening depthfirst search  3.4, HW1 Starter Code and Documentation  
3  31  Uninformed Search: repeated state detection, inclass implementation  
4  February  2  Informed Search: heuristic search, bestfirst search, uniform cost search, greedy search  rest of Ch. 3 except 3.5.3 (memorybounded heuristic search) 
5  7  Stochastic Local Search: hill descent with uphill step probability, simulated annealing, parameter tuning, demonstrations  4.1, Skim Science Simulated Annealing article  
6  9  Stochastic Local Search: inclass implementations and challenge problem  (4.2)  
7  14  GameTree Search: minimax, alphabeta pruning  5.15.3  
8  16  GameTree Search: heuristic evaluation, inclass heuristic development  5.4  
9  21  GameTree Search: time management  H. Baier, M. Winands. Time Management for MonteCarlo Tree Search in Go. (skim time management strategies)  
10  23  GameTree Search: expectiminimax, inclass development  5.5  
11  28  GameTree Search: Monte Carlo Tree Search (MCTS), UCT  L. Kocsis, C. Szepesvari. Bandit based MonteCarlo Planning. (C. Browne et al. A Survey of Monte Carlo Tree Search Methods)  
12  March  2  Machine Learning: Dynamic Programming  "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", Sections 12 
13  7  Machine Learning: Dynamic Programming (cont.), Value Iteration  "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", remainder; Russell & Norvig sections 17.117.2  
14  9  Machine Learning: Value Iteration (cont.)  
15  21  Inclass Midterm  
16  23  Knowledge Representation & Reasoning: Propositional Logic, syntax, semantics, truth assignments, models, (un)satisfiability, validity, entailment, equivalence.  7.17.4 (through "A simple knowledge base"), "Clue Deduction: an introduction to satisfiability reasoning" sections 12  
17  28  Knowledge Representation & Reasoning: conjunctive normal form, resolution theorem proving, various logic problems  7.5, "Clue Deduction: an introduction to satisfiability reasoning" sections 36  
18  30  Knowledge Representation & Reasoning: Clue project knowledge base, inclass play and reasoning about Clue  "Clue Deduction: an introduction to satisfiability reasoning" section 8  
19  April  4  Knowledge Representation & Reasoning: inclass knowledge representation exercise, conversion to DIMACS CNF format, DPLL algorithm  http://en.wikipedia.org/wiki/DPLL_algorithm 
20  6  Knowledge Representation & Reasoning: stochastic local search for boolean satisfiability, WalkSAT, inclass simplified WalkSAT solver programming  7.6.2 (local search algorithms), http://en.wikipedia.org/wiki/WalkSAT  
21  11  Data Mining: kMeans Clustering  Unsupervised learning definition 18.1,
kMeans Clustering
Wikipedia article, OnMyPhD demo apps 

22  13  Data Mining: kMeans Clustering, determining the number of clusters, the gap statistic  Determining the number of cluster Wikipedia article; Tibshirani, Walther, and Hastie. "Estimating the number of clusters in a data set via the gap statistic"  
23  18  Probabilistic Reasoning: Bayesian Networks, Gibbs Sampling, a Markov Chain Monte Carlo (MCMC) algorithm for reasoning on Bayesian networks  14.114.2, An Introduction to Monte Carlo Techniques in AI  Part II, 14.5.2, Pearl, Judea. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Revised 2nd Printing, Morgan Kaufmann, San Francisco, California, USA, 1988, Table 1 (p. 197), section 4.4.3 (pp. 210216, up to "Justifying the Computations")  
24  20  Robotics: Monte Carlo Localization  S. Thrun., W. Burgard, and D. Fox. Probabilistic Robotics (draft), Chapter 8  
25  25  Motion models (Robotics project work)  S. Thrun., W. Burgard, and D. Fox. Probabilistic Robotics (draft), Chapter 5  
26  27  Sensor models (Robotics project work)  S. Thrun., W. Burgard, and D. Fox. Probabilistic Robotics (draft), Chapter 6  
27  May  2  Course Evaluations  
28  4  Robotics Team Demonstrations  
Final Exam Schedules (Fall, Spring) Thursday, May 11, 8:30 a.m.  11:30 a.m. 