Tentative Schedule
Date  Topic  Materials  Homework 
Jan. 7  Course overview. What is AI? Brief history.  Lecture 1 slides RN, 3rd or 2nd ed. Chapter 1, 2 Turing's 1950 paper ALICE the chatbot Julia the chatbot Video of Claude Shannon's juggling robot DARPA grand challenge and Urban challenge Deep Blue Polaris, the pokerplaying program Tom Mitchell's mind reading project  
Jan. 9  Search  Lecture 2 slides RN, 3rd ed. Sec. 3.13.3 or RN 2nd ed. Sec. 3.13.5 Uninformed search applets Graph search tool 

Jan.14  Search  Lecture 3 slides RN, 3rd ed. Sec. 3.53.6 or RN 2nd ed. Sec. 4.14.2 Paper on realtime A* Applet for visualizing different search algorithms 
Homework 1 posted 
Jan.16  Search  Lecture 4 slides RN, 3rd ed. Sec. 4.14.2 or RN 2nd ed. Sec. 4.34.4 Travelling salesman problem An applet for TSP using simulated annealing 

Jan.21  Genetic algorithms. Constraint satisfaction problems.  Lecture 5 slides RN, 3rd ed. Chapter 6 or 2nd ed.Chapter 5 TSP using genetic algorithms CSP solver demo CSP solver using stochastic local search Nqueens demo 

Jan.23  Game playing. Adversarial search  Lecture 6 slides RN, 3rd ed. Sec. 5.15.4 or 2nd ed. Sec. 6.16.4 Deep Blue Samuel's early checkers player Schaeffer et al. checkers solution Logistello 
Package to generate random mazes and test program 
Jan.28  Game playing. Adversarial search  Lecture 7 slides Readings to come 
Homework 1 (written questions) due 
Jan.30  Logic and planning  Lecture 8 slides RN, 3rd or 2nd ed. Sec. 8.18.3, 9.19.5 A Wumpus world demo Another Wumpus world, with different kinds of agents 
Homework 1 programming due 
Feb. 4  Logic and planning  Lecture 9 slides 
Homework 2 posted 
Feb. 6  Representing uncertainty. Probabilities and conditional probabilities. Bayes rule  Lecture 10 slides RN, 3rd or 2nd ed. Chapter 13 Bayes rule applet 

Feb.11  No class  
Feb.13  Bayes nets (representation, examples, inference)  Lecture 11 (done on the board, typed notes to be posted) 
Homework 2 due 
Feb.18  Guest lecture: Prof. Greg Dudek  Lecture 12  
Feb.20  Guest lecture: Prof. Joe Vybihal  Lecture 13  
Feb.25  Making decisions under uncertainy: Utilities. Decision graphs. Introduction to bandit problems  Lecture 14  
Feb.27  Inclass midterm examination  Covering lectures up to and including February 6 Midterm from 2010 and solutions More example questions and solutions You are allowed one doublesided cheat sheet. 

Mar. 4  Spring break  
Mar. 6  Spring break  
Mar.11  Bandit problems: Exploration vs exploitation tradeoff. Sequential decision making. Markov chains. Markov Decision Processes.  Lecture 15 slides  Homework 3 posted 
Mar.13  More on MDPs. Policies and value functions. Dynamic programming  Lecture 16 slides 

Mar.18  More on MDPs. Introduction to reinforcement learning  Lecture 17 slides 

Mar.20  Reinforcement learning (previous lecture)  Homework 3 due 

Mar.25  More on learning. Linear function approximation. Gradient descent  Lecture 18 slides RN 3rd ed. or 2nd ed. Sec. 18.3 

Mar.27  Neural networks  Lecture 19 slides 

Apr. 1  Easter Monday  
Apr. 3  Wrapup of neural nets. Clustering 
Lecture 20 slides 

Apr. 8  Special topics: case studies of AI applied to games  Homework 4 posted  
Apr.10  Special topics: Natural language processing  Lecture 22 slides  
Apr. 15  Wrapup: Philosophy and future of AI  Lecture 23 slides RN 3rd ed. or 2nd ed. Chapter 26, 27 Final exam from 2010 
Homework 4 due; Project due (no penalties until April 22) 