Course Schedule
CSC 261 Artificial Intelligence Fall 2015

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.

Important: With the exception of exam dates, this schedule is tentative,. Topics and activities for a subsequent class may be considered finalized by the end of the previous class. For example, Wednesday's reading is final immediately after Monday's class (because Tuesday is lab day).

Skip to week: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Final

Week Day Date Topic Reading Assignment
0 Fri 8/28 Introduction to AI RN 1 pp. 1-30
1 Mon 8/31 Intelligent Agents RN 2.0-2.3 pp. 34-46 Lab 0 (Due 8/30)
Tue 9/1 Lab: Recalling Scheme Lab 1
Wed 9/2 Agent Structure RN 2.4-2.5 pp. 46-59
Fri 9/4 Search RN 3.0-3.3 pp. 64-81
2 Mon 9/7 Uninformed Search RN 3.4 pp. 81-91 Lab 1
Tue 9/8 Lab: Uninformed Search Lab 2
Wed 9/9 Informed Search RN 3.5-3.5.2,3.6-3.7 pp. 92-99, 102-109
Fri 9/11 Local Search RN 4.0-4.2 pp. 120-132 (to "step size")
3 Mon 9/14 Nondeterministic Environments, Partial Observations RN 4.3-4.4 pp. 133-146 Lab 2
Tue 9/15 Lab: Heuristic Search Lab 3
Wed 9/16 Online Search RN 4.5-4.6 pp. 147-154
Fri 9/18 Adversarial Search RN 5.0-5.4.2, 5.9 pp. 161-176, 189-190
4 Mon 9/21 Exam 1 RN 2.0-4.6 Lab 3
Tue 9/22 Lab: Adversarial Search Lab 4
Wed 9/23 Propositional Logic RN 7.0-7.4 pp. 234-249
Fri 9/25 Propositional Inference RN 7.5-7.5.2 pp. 249-256
5 Mon 9/28 Propositional Inference: Chaining RN 7.5.3-7.5.4, 7.8 pp. 256-259, 274-275 Lab 4
Tue 9/29 Lab: Propositional Logic Lab 5
Wed 9/30 First-Order Logic RN 8.0-8.3, 8.5 pp. 285-306, 313
Fri 10/2 FOL Inference RN 9.0-9.3.2 pp. 322-333
6 Mon 10/5 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 Lab 5
Tue 10/6 Lab: First-Order Logic Lab 6
Wed 10/7 Probability ETJ 1.1-1.4, 1.7 pp. 1-6, 13-19; RN 13.0-13.3 pp. 480-494
Fri 10/9 Independence and Bayes' Rule RN 13.4-13.5, 13.7 pp. 494-499, 503
7 Mon 10/12 Independence and Bayes' Rule (cont.) Lab 6
Tue 10/13 Bayesian Learning RN 18.0-18.2 pp. 693-697; ETJ 4 pp. 401-409
Wed 10/14 Bayesian Learning, cont. ETJ 4, pp. 410-418
Fri 10/16 Exam 2 RN 5.0-13.3, ETJ 1, LM
Enjoy Fall Break!
8 Mon 10/26 Bayesian Networks RN 14.0-14.2 pp. 510-518
Tue 10/27 Lab: Probability Lab 7
Wed 10/28 Bayes Net Inference RN 14.4-14.4.2 pp. 522-528
Fri 10/30 Dynamic Bayes Nets RN 15.0-15.1 pp. 566-570
9 Mon 11/2 DBN Inference RN 15.2 pp. 570-578 Lab 7
Tue 11/3 Lab: Hidden Markov Models Lab 8
Wed 11/4 Pause for breath
Fri 11/6 Evaluating Hypotheses RN 18.4.0, 18.4.2 pp. 708-709, 710-712
10 Mon 11/9 Exam 3 RN 13.4-15.2, 18.0-18.2; ETJ 1,4
Tue 11/10 Lab 8 (cont.)
Wed 11/11 Learning with Linear Models RN 18.6-18.6.1, 18.6.3-18.6.4, pp. 717-720, 723-725
Fri 11/13 Lab: Decision Trees RN 18.3-18.3.4, pp. 697-704; Lab 9, Introduction & Background, pp. 1-5 Lab 8
11 Mon 11/16 Lab 9 (cont.)
Tue 11/17 Making Decisions RN 16.0-16.3 pp. 610-621
Wed 11/18 Decision Networks and Value of Information RN 16.5-16.8 pp. 626-636
Fri 11/20 Lab: Decision Tree Analysis Lab 10 Lab 9
12 Mon 11/23 Markov Decision Processes RN 17.1-17.2.2, 17.3 pp. 645-654, 656-658
Tue 11/24 Passive Reinforcement Learning RN 21.1-21.2 pp. 830-838
Wed 11/25 Active Reinforcement Learning RN 21.3 839-845
Fri 11/27 Thanksgiving Break
13 Mon 11/30 Colton et al. & Boden (2009) Creativity Lab 10
Tue 12/1 Lab: Markov Decision Processes Lab 11
Wed 12/2 Lab 11 (cont.)
Fri 12/4 Reinforcement Learning RN 21.1-21.3
14 Mon 12/7 Philosophical Foundations RN 26 pp. 1020-1040
Tue 12/8 AI Today RN 27 pp. 1044-1052
Wed 12/9 Research Priorities Russell et al.
Fri 12/11 Wrap-Up Lab 11
F Fri 12/18 Final Exam (2 p.m.)
Jerod Weinman
Created 10 August 2015