Schedule - CSE 4308 - 001 and CSE 5360 - 001

L/no	Date	Chapters, 2nd Edition	Chapters, 3rd Edition	Topic
1	01/14	1, 2	1, 2	Course Details[1] and Overview[1], Agents[2]
2	01/16	3	3.1 - 3.4	Solving Problems by Search[2]
	01/21			MARTIN LUTHER KING DAY - NO CLASS
3	01/23	4	3.5 - 3.6	Notes on Implementation[1], Informed Search[2]
4	01/28	6	5	Game Playing[2]
5	01/30	6	5	Alpha - Beta Pruning[3], Non - Deterministic Games
6	02/04	5	6	Constraint Satisfaction Problems[2]
7	02/06	7	7	Knowledge and Logic Reasoning[2]
8	02/11			Exam 1 Review
	02/13			EXAM 1
9	02/18	7	7	Exam 1 Discussion, Inference by Enumeration
10	02/20	7	7	Forward and Backward Chaining, Resolution
11	02/25	8.1-8.3, 9.1.2, 9.2	8.1-8.3, 9.1.2, 9.2	First Order Logic[3], Unification & FC/BC[2]
12	02/27	11	10	Planning[3], Planning[2]
13	03/04	11	10	Planning (Contd.)
14	03/06	12	11	Conditional Planning and Replanning[3], Conditional Planning and Replanning[2]
	03/11			SPRING BREAK - NO CLASS
	03/13			SPRING BREAK - NO CLASS
15	03/18	13	13	Probablity[2]
16	03/20			Exan 2 Review
	03/25			EXAM 2
17	03/27	13	13	Exam 2 Discussion,, Joint Probablity Distribution
18	04/01	13	13	Prior and Posterior Probablites[3]
19	04/03	14.1 - 14.4	14.1 - 14.4	Bayesian Networks[3], Bayesian Networks[2]
20	04/08	18.1-18.3	18.1-18.3	Learning[3], Learning[2]
21	04/10	18.1-18.3	18.1-18.3	Decision Trees[3]
22	04/15	18.1-18.3	18.1-18.3	Practical Issues with Decision Trees[3]
23	04/17		20.3	Bayesian Classifiers[3]
24	04/22		18.8.1	Probabilty Estimations, Nearest Neighbor Classifiers[3]
25	04/24	20.5	18.6.4, 18.7	Neural Networks[2], Neuron Architeture[1][4],  Backpropogation Learning[3]
26	04/29	20.5	18.6.4, 18.7	Backpropogation Learning, Introduction to Deep Learning
27	05/01			Exam 3 Review, Final Q & A
	05/08			EXAM 3 - CSE 5360 001, 002
	05/10			EXAM 3 - CSE 4308 001

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, University of Texas at Arlington.
[2] (c) Stuart Russell (Artificial Intelligence: A Modern Approach. Stuart Russell, Peter Norvig. ISBN: 978-0136042594)
[3] (c) Vassilis Athitsos, University of Texas at Arlington.
[4] (c) Martin Hagan (Neural Network Design. Martin T. Hagan, Howard B. Demuth, Mark H. Beale. ISBN: 0-9717321-0-8)