Introduction to AI (COMP-424)
Winter 2013

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 poker-playing program
Tom Mitchell's mind reading project
Jan. 9 Search  Lecture 2 slides
RN, 3rd ed. Sec. 3.1-3.3 or RN 2nd ed. Sec. 3.1-3.5
Uninformed search applets
Graph search tool
Jan.14 Search Lecture 3 slides
RN, 3rd ed. Sec. 3.5-3.6 or RN 2nd ed. Sec. 4.1-4.2
Paper on real-time A*
Applet for visualizing different search algorithms
Homework 1 posted
Jan.16 Search Lecture 4 slides
RN, 3rd ed. Sec. 4.1-4.2 or RN 2nd ed. Sec. 4.3-4.4
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
N-queens demo
Jan.23 Game playing. Adversarial search Lecture 6 slides
RN, 3rd ed. Sec. 5.1-5.4 or 2nd ed. Sec. 6.1-6.4
Deep Blue
Samuel's early checkers player
Schaeffer et al. checkers solution
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. 8.1-8.3, 9.1-9.5
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 In-class midterm examination Covering lectures up to and including February 6
Midterm from 2010 and solutions
More example questions and solutions
You are allowed one double-sided cheat sheet.
Mar. 4Spring break
Mar. 6Spring break
Mar.11 Bandit problems: Exploration vs exploitation trade-off. 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 Wrap-up 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 Wrap-up: 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)