Course Schedule
CSC 261 Artificial Intelligence Fall 2011

Note: Homework is shown in the assignment column on the day it is due (not the day assigned).

Note: Readings are RN="Russell & Norvig", LM="Lee & Mead", and ETJ="Jaynes". LM and ETJ may be found under the Documents section of the PioneerWeb for this course.

Week Day Date Topic Reading Assignment
Fri 8/26 Introduction to AI RN 1 pp. 1-30
1 Mon 8/29 Intelligent Agents RN 2.0-2.3 pp. 34-46 Lab 0
Tue 8/30 Lab: Recalling Scheme Lab 1
Wed 8/31 Agent Structure RN 2.4-2.5 pp. 46-59
Fri 9/2 Search RN 3.0-3.3 pp. 64-81
2 Mon 9/5 Uninformed Search RN 3.4 pp. 81-91 Lab 1
Tue 9/6 Lab: Uninformed Search Lab 2
Wed 9/7 Informed Search RN 3.5-3.7 pp. 92-109
Fri 9/9 Local Search RN 4.0-4.2 pp. 120-132 (to "step size")
3 Mon 9/12 Nondeterministic Environments, Partial Observations RN 4.3-4.4 pp. 133-146 Lab 2
Tue 9/13 Lab: Informed Search Lab 3
Wed 9/14 Online Search RN 4.5-4.6 pp. 147-154
Fri 9/16 Adversarial Search RN 5.0-5.3 pp. 161-171
4 Mon 9/19 Real Time Decisions RN 5.4, 5.9 pp. 171-176, 189-190 [OPTIONAL: RN 5.5-5.8] Lab 3
Tue 9/20 Lab: Adversarial Search Lab 4
Wed 9/21 Propositional Logic RN 7.0-7.4 pp. 234-249
Fri 9/23 Propositional Inference RN 7.5-7.5.2 pp. 249-256
5 Mon 9/26 Propositional Inference: Chaining RN 7.5.3-7.5.4, 7.8 pp. 256-259, 274-275 Lab 4
Tue 9/27 Lab: Propositional Inference Lab 5
Wed 9/28 TBD
Fri 9/30 Exam 1 RN 1.0-7.5.2
6 Mon 10/3 First-Order Logic RN 8.0-8.3, 8.5 pp. 285-306, 313 Lab 5
Tue 10/4 Lab: Resolution and FOL Lab 6
Wed 10/5 FOL Inference RN 9.0-9.3.2 pp. 322-333
Fri 10/7 Backward Chaining and Prolog RN 9.4-9.4.2, 9.4.4 pp. 337-342; LM pp. 1-11 Skip (or skim) RN 9.4.3
7 Mon 10/10 FOL Resolution RN 9.5-9.5.3, 9.5.6-9.6 pp. 345-350, 355-357 Lab 6
Tue 10/11 Lab: Prolog Lab 7
Wed 10/12 Probability ETJ 1.1-1.4, 1.7; RN 13.0-13.3 pp. 480-494
Fri 10/14 Independence and Bayes' Rule RN 13.4-13.5, 13.7 pp. 494-499, 503
Enjoy Fall Break!
8 Mon 10/24 Bayesian Networks RN 14.0-14.2 pp. 510-518 Lab 7
Tue 10/25 Lab: Probability Lab 8
Wed 10/26 Bayes Net Inference RN 14.4-14.4.1 pp. 522-524; BP 1-2, pp. 1-6
Fri 10/28 Belief Propagation BP 3-5, pp. 6-13
9 Mon 10/31 Dynamic Bayes Nets RN 15.0-15.2.2 pp. 566-576 Lab 8
Tue 11/1 Lab: Bayesian Network Inference Lab 9
Wed 11/2 Learning from Examples RN 18.0-18.2, 18.4.0, 18.4.2, pp. 693-697, 708-709, 710-712
Fri 11/4 Bayesian Learning RN 20.0-20.1, 20.2.2 pp. 802-805, 808-809
10 Mon 11/7 Lab: Decision Trees RN 18.3-18.3.4, pp. 697-704; Lab 10: Decision Trees, pp. 1-5 Lab 9
Tue 11/8 Lab: Decision Trees Lab 10
Wed 11/9 TBD
Fri 11/11 Exam 2 RN 7.5.3-20.2.2
11 Mon 11/14 Learning with Linear Models RN 18.6-18.6.1, 18.6.3-18.7.3, pp. 717-720, 723-732 Lab 10
Tue 11/15 Lab: Logistic Regression Lab 11
Wed 11/16 Making Decisions RN 16.0-16.3 pp. 610-621
Fri 11/18 Decision Networks and Value of Information RN 16.5-16.8 pp. 626-636
12 Mon 11/21 Markov Decision Processes RN 17.1-17.2.2, 17.3 pp. 645-654, 656-658 Lab 11
Tue 11/22 Lab: Value Iteration Lab 12
Wed 11/23 Passive Reinforcement Learning RN 21.1-21.2 pp. 830-838
Fri 11/27 Thanksgiving Break
13 Mon 11/28 Active Reinforcement Learning RN 21.3 839-845 Lab 12
Tue 11/29 Lab: Policy Iteration and Passive RL Lab 13
Wed 11/30 Video Game AI Bakkes et al.
Fri 12/2 Building Watson Ferrucci et al.
14 Mon 12/5 Weak and Strong AI RN 26 pp. 1020-1040 Lab 13
Tue 12/6 Lab: Reinforcement Learning Lab 14
Wed 12/7 TBD
Fri 12/9 Wrap-Up RN 27 pp. 1044-1052
F Tue 12/13 Final Exam (9 a.m.)
Fri 12/16 Lab 14 (noon)
Jerod Weinman
Created 15 August 2011