L/no Date Chapters, 2nd Edition Chapters, 3rd Edition Topic
1 Wed 01/18 1, 3 1, 3.1 - 3.4 Course Details[1] and Overview[1], Solving Problems by Search[2]
2 Mon 01/23 1, 3 1, 3.1 - 3.4 Solving Problems by Search[2] (Contd.), Implementation Notes[3]
3 Wed 01/25 4 3.5 - 3.6 Informed Search[3]
4 Mon 01/30 2 2 Agents[2], Game Playing[2]
5 Wed 02/01 2 2 Game Playing[2] (Contd.)
6 Mon 02/06 6 5 Alpha - Beta Pruning[3], Non - Deterministic Games
7 Wed 02/08 7 7 Knowledge and Logic Reasoning[2]
8 Mon 02/13 7 7 Inference by Enumeration
9 Wed 02/15 7 7 Exam 1 Material Review, Inference (Contd.)
Mon 02/20 EXAM 1
10 Wed 02/22 8.1-8.3, 9.1 8.1-8,3, 9.1 Exam 1 Discussion, First Order Logic[3]
11 Mon 02/27 8.1-8.3, 9.1 8.1-8.3, 9.1 First Order Logic[3] (Contd.)
12 Wed 03/01 11 10 Planning[3]
13 Mon 03/06 11 10 Planning[2](Contd.)
14 Wed 03/08 12 11 Conditional Planning and Replanning[3], Conditional Planning and Replanning[2]
Mon 03/13 SPRING BREAK - No Class
Wed 03/15 SPRING BREAK - No Class
15 Mon 03/20 13 13 Probability[2]
16 Wed 03/22 13 13 Exam 2 Material Review, Probability[2] (Contd.), Joint Probability Distributions

Mon 03/27

EXAM 2
17 Wed 03/29 13 13 Exam 2 Discussion, Prior and Posterior probablities[3]
18 Mon 04/03 14.1-14.4 14.1-14.4 Bayesian Networks[2], Bayesian Networks[3]
19 Wed 04/05 18.1-18.3 18.1-18.3 Learning [3], Learning Methods[2]
20 Mon 04/10 18.1-18.3 18.1-18.3 Decision Trees[3]
21 Wed 04/12 18.1-18.3 18.1-18.3 Practical Issues with Decision Trees[3]
22 Mon 04/17
20.3 Bayesian Classifiers, Probability Estimation[3]
23 Wed 04/19  18.8.1 Nearest Neighbor Classifiers[3]
24 Mon 04/24 20.5 18.6.4, 18.7 Neural Networks[2] (Addlitional Slides 1[1][4], Additional Slides 2[4]), Backpropogation Learning[3]
25 Wed 04/26 20.5 18.6.4, 18.7 Exam 3 Material Review, Backpropogation Learning[3] (Contd.)
Mon 05/01 EXAM 3
26 Wed 05/03 Exam 3 Discussion, Optional Assignment Information, Final Q&A
Mon 05/08 MAKEUP EXAM
Wed 05/10 FINALS WEEK - No Class


This schedule is tentative and subject to change. If changes are necessary they will be announced in class and posted here.
[1] (c) Vamsikrishna Gopikrishna
[2] (c) Stuart Russell (Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig. ISBN: 978-0136042594)
[3] (c) Vassilis Athitsos
[4] (c) Martin Hagan (Neural Network Design. Martin T. Hagan, Howard B. Demuth, Mark H. Beale. ISBN: 0-9717321-0-8)