Course Schedule
CSC 261 Artificial Intelligence Spring 2018

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="Lu & 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
1 Mon 1/22 Introduction to AI RN 1 pp. 1-30 Lab 0
Tue 1/23 Intelligent Agents RN 2.0-2.3 pp. 34-46
Wed 1/24 Agent Structure RN 2.4-2.5 pp. 46-59
Fri 1/26 Search RN 3.0-3.3 pp. 64-81
2 Mon 1/29 Uninformed Search RN 3.4 pp. 81-91 Lab 1
Tue 1/30 Lab: Uninformed Search Lab 2
Wed 1/31 Informed Search RN 3.5-3.5.2,3.6-3.7 pp. 92-99, 102-109
Fri 2/2 Local Search RN 4.0-4.2 pp. 120-132 (to "step size")
3 Mon 2/5 Nondeterministic Environments, Partial Observations RN 4.3-4.4 pp. 133-146 Lab 2
Tue 2/6 Lab: Heuristic Search Lab 3
Wed 2/7 Online Search RN 4.5-4.6 pp. 147-154
Fri 2/9 Adversarial Search RN 5.0-5.4.2, 5.9 pp. 161-176, 189-190
4 Mon 2/12 Propositional Logic RN 7.0-7.4 pp. 234-249 Lab 3
Tue 2/13 Lab: Adversarial Search Lab 4
Wed 2/14 Propositional Inference RN 7.5-7.5.2 pp. 249-256
Fri 2/16 Propositional Inference: Chaining RN 7.5.3-7.5.4, 7.8 pp. 256-259, 274-275
5 Mon 2/19 First-Order Logic RN 8.0-8.3, 8.5 pp. 285-306, 313 Lab 4
Tue 2/20 Lab: Propositional Logic Lab 5
Wed 2/21 FOL Inference RN 9.0-9.3.2 pp. 322-333
Fri 2/23 Exam 1 RN 2.0-5.4
6 Mon 2/26 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
Tue 2/27 Lab: First-Order Logic Lab 6
Wed 2/28 Probability ETJ 1.1-1.4, 1.7 pp. 1-6, 13-19; RN 13.0-13.3 pp. 480-494 Lab 5
Fri 3/2 Independence and Bayes' Rule RN 13.4-13.5, 13.7 pp. 494-499, 503
7 Mon 3/5 Independence and Bayes' Rule (cont.)
Tue 3/6 Bayesian Learning RN 18.0-18.2 pp. 693-697; ETJ 4 pp. 401-409
Wed 3/7 Bayesian Learning, cont. ETJ 4, pp. 410-418 Lab 6
Fri 3/9 Lab: Probability Lab 7
8 Mon 3/12 Bayesian Networks RN 14.0-14.2 pp. 510-518
Tue 3/13 Bayes Net Inference RN 14.4-14.4.2 pp. 522-528
Wed 3/14 Dynamic Bayes Nets RN 15.0-15.1 pp. 566-570 Lab 7
Fri 3/16 Exam 2 RN 7.0-13.7, ETJ 1, LM
Enjoy Spring Break!
9 Mon 4/2 DBN Inference RN 15.2.0-15.2.2 pp. 570-575
Tue 4/3 Lab: Hidden Markov Models Lab 8
Wed 4/4 DBN Explanation RN 15.2.3 pp. 576-578
Fri 4/6 Evaluating Hypotheses RN 18.4.0, 18.4.2 pp. 708-709, 710-712
10 Mon 4/9 Decision Trees RN 18.3-18.3.4, pp. 697-704; Lab 9, Introduction & Background, pp. 1-5 Lab 8
Tue 4/10 Lab: Decision Trees Lab 9
Wed 4/11 Making Decisions RN 16.0-16.3 pp. 610-621
Fri 4/13 Decision Networks and Value of Information RN 16.5-16.8 pp. 626-636
11 Mon 4/16 Markov Decision Processes RN 17.1-17.2.2, 17.3 pp. 645-654, 656-658 Lab 9
Tue 4/17 Decision Tree Analysis Lab 10
Wed 4/18 Passive Reinforcement Learning RN 21.1-21.2 pp. 830-838
Fri 4/20 Exam 3 RN 14.0-15.2, 18.0-18.4; ETJ 4
12 Mon 4/23 Active Reinforcement Learning RN 21.3, pp. 839-845 Lab 10
Tue 4/24 Lab: Markov Decision Processes Lab 11
Wed 4/25 Lab 11 (cont.)
Fri 4/27 Learning with Linear Models RN 18.6.0-18.6.1, 18.6.3-18.6.4, pp. 717-720, 723-725
13 Mon 4/30 Neural Networks RN 18.7-18.7.4, pp. 727-736 Lab 11
Tue 5/1 Lab: Reinforcement Learning Lab 12
Wed 5/2 Neural Network Learning RN 18.7.4-18.7.5, pp. 733-737
Fri 5/4 Deep Reinforcement Learning Mnih et al.
14 Mon 5/7 Philosophical Foundations RN 26 pp. 1020-1040
Tue 5/8 AI Today RN 27 pp. 1044-1052
Wed 5/9 Research Priorities Russell et al.
Fri 5/11 Wrap-Up Lab 12
F
01 Fri 5/18 Final Exam (9 a.m.)
02 Thu 5/17 Final Exam (9 a.m.)
Jerod Weinman
Created 23 November 2017