Course Schedule
CSC 261 Artificial Intelligence Spring 2020

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 1/20 No Class (MLK Day) RN 1 pp. 1-30
Tue 1/21 Intelligent Agents RN 2.0-2.3 pp. 34-46 Lab 0
Wed 1/22 Agent Structure RN 2.4-2.5 pp. 46-59
Fri 1/24 Search RN 3.0-3.3 pp. 64-81
2 Mon 1/27 Uninformed Search RN 3.4 pp. 81-91 Lab 1
Tue 1/28 Lab: Uninformed Search Lab 2
Wed 1/29 Informed Search RN 3.5-3.5.2,3.6-3.7 pp. 92-99, 102-109
Fri 1/31 Local Search RN 4.0-4.2 pp. 120-132 (to "step size")
3 Mon 2/3 Nondeterministic Environments, Partial Observations RN 4.3-4.4 pp. 133-146 Lab 2
Tue 2/4 Lab: Heuristic Search Lab 3
Wed 2/5 Online Search RN 4.5-4.6 pp. 147-154
Fri 2/7 Adversarial Search RN 5.0-5.4.2, 5.9 pp. 161-176, 189-190
4 Mon 2/10 Propositional Logic RN 7.0-7.4 pp. 234-249 Lab 3
Tue 2/11 Lab: Adversarial Search Lab 4
Wed 2/12 Propositional Inference RN 7.5-7.5.2 pp. 249-256
Fri 2/14 Propositional Inference: Chaining RN 7.5.3-7.5.4, 7.8 pp. 256-259, 274-275
5 Mon 2/17 First-Order Logic RN 8.0-8.3, 8.5 pp. 285-306, 313 Lab 4
Tue 2/18 Lab: Propositional Logic Lab 5
Wed 2/19 FOL Inference RN 9.0-9.3.2 pp. 322-333
Fri 2/21 Exam 1 RN 2.0-5.4
6 Mon 2/24 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 2/25 Lab: First-Order Logic Lab 6
Wed 2/26 Probability ETJ 1.1-1.4, 1.7 pp. 1-6, 13-19; RN 13.0-13.3 pp. 480-494 Lab 5
Fri 2/28 Independence and Bayes' Rule RN 13.4-13.5, 13.7 pp. 494-499, 503
7 Mon 3/2 Independence and Bayes' Rule (cont.)
Tue 3/3 Bayesian Learning RN 18.0-18.2 pp. 693-697; ETJ 4 pp. 401-409
Wed 3/4 Bayesian Learning, cont. ETJ 4, pp. 410-418 Lab 6
Fri 3/6 Lab: Probability Lab 7
8 Mon 3/9 Bayesian Networks RN 14.0-14.2 pp. 510-518
Tue 3/10 Bayes Net Inference RN 14.4-14.4.2 pp. 522-528
Wed 3/11 Dynamic Bayes Nets RN 15.0-15.1 pp. 566-570
Fri 3/13 Exam 2 RN 7.0-13.7, ETJ 1, LM
Enjoy Spring Break!
9 Mon 3/30 DBN Inference 📽 RN 15.2.0-15.2.2 pp. 570-575
Tue 3/31 Lab: Hidden Markov Models📽 Lab 8
Wed 4/1 DBN Explanation 📽 RN 15.2.3 pp. 576-578
Fri 4/3 Evaluating Hypotheses 📽 RN 18.4.0, 18.4.2 pp. 708-709, 710-712 Lab 7
10 Mon 4/6 Decision Trees 📽 RN 18.3-18.3.4, pp. 697-704; Lab 9, Introduction & Background, pp. 1-5
Tue 4/7 Lab: Decision Trees Lab 9
Wed 4/8 Making Decisions 📽 RN 16.0-16.3 pp. 610-621
Fri 4/10 Decision Networks and Value of Information 📽 RN 16.5-16.8 pp. 626-636
11 Mon 4/13 Markov Decision Processes 📽 RN 17.1-17.2.2, 17.3 pp. 645-654, 656-658 Lab 9
Tue 4/14 Decision Tree Analysis Lab 10
Wed 4/15 Passive Reinforcement Learning 📽 RN 21.1-21.2 pp. 830-838
Fri 4/17 Exam 3 RN 14.0-15.2, 18.0-18.4; ETJ 4
12 Mon 4/20 Active Reinforcement Learning 📽 RN 21.3, pp. 839-845 Lab 10
Tue 4/21 Lab: Markov Decision Processes Lab 11
Wed 4/22 Lab 11 (cont.)
Fri 4/24 Learning with Linear Models 📽 RN 18.6.0-18.6.1, 18.6.3-18.6.4, pp. 717-720, 723-725
13 Mon 4/27 Neural Networks 📽 RN 18.7-18.7.4, pp. 727-736 Lab 11
Tue 4/28 Lab: Reinforcement Learning Lab 12
Wed 4/29 Neural Network Learning 📽 RN 18.7.4-18.7.5, pp. 733-737
Fri 5/1 Philosophical Foundations RN 26 pp. 1020-1040
14 Mon 5/4 AI Today RN 27 pp. 1044-1052
Tue 5/5 Research Priorities Russell et al.
Wed 5/6 Deep Reinforcement Learning Mnih et al.
Fri 5/8 Wrap-Up Questions Lab 12
F Thu 5/14 Final Exam (2 p.m.)
Jerod Weinman
Created 6 November 2020