CSE841-Fall2007

CSE841: Artificial Intelligence

Fall 2007

Time:	Tuesday and Thursday 3:00-4:20pm
Location:	151 Communication Arts Bldg
Professor:	Joyce Chai, 2138 Engineering Building, (517)-432-9239, jchai AT cse DOT msu DOT edu
Office Hours:	Tuesday and Thursday 4:30-5:30pm, or by appointment

Course Description:

The graduate-level course provides a survey to several topics in the field of Artificial Intelligence. Specific topics including search, logical inference, knowledge representation, probabilistic reasoning, inductive learning, and reinforcement learning. These topics will be examined through reading, discussion, and hands-on experience with AI applications.

Text book:

Artificial Intelligence, Russell and Norvig, Prentice-Hall, 2003 (edition 2). It should be available from the MSU bookstore, as well as from Amazon and other online providers. More information about the text book and related information is provided here.

Optional books (reserved in the Engineering Library)

Machine Learning, by Tom Mitchell, McGraw Hill, 1997 (ML)
Programming in Prolog: Using the ISO Standard, by W. F. clocksin and C. S. Mellish, Springer; 5th edition, 2003 (PP)
Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, by I. Witten and E. Frank, Morgan Kaufmann; 2nd edition (2005) (DM)

Course Grades:

Attendance and class contribution	5%
Paper critiques	10%
Four Homework Assignments	50%
Midterm Exam	15%
Final Project	20%

Course work :

The work in this course consists of four homework assignments, three paper critiques, one midterm exam, and one final project.

Each homework assignment includes a written part and a programming part. The written portion must be turned in at the beginning of the lecture on the day it is due. The programming part is due at 11:59pm of the due date (submitted through handin facility). No late homework will be accepted.
For each assigned paper, a one page (400-500 words) critique must be turned in at the beginning of the lecture on the day it is due.
The final project includes three parts: project proposal, project presentation, and final report. You are encouraged to start thinking of the project early. Please consult with the instructor on the topics of your project.

	Assigned date	Due date
Homework 1	Aug. 30	Sept. 13
Homework 2	Sept. 18	Oct. 9
Homework 3	Oct. 9	Nov. 1
Homework 4	Nov. 9	Nov. 30
Paper 1	-	Sept. 4
Paper 2	-	Sept. 27
Paper 3	-	Oct. 18
Midterm Exam	-	November 1
Project Proposal	-	November 6
Project Report	-	December 12

Schedule of Topics

Week	Class Date	Topic	Chapter Reference	Related information/reading and Important dates
1	Aug 28	Introduction	RN Chapter 1, 2, Chapter 3.1-3.3	Computing Machinery and Intelligence, by A. M. Turing The Chinese Room Argument
	Aug 30	Search	RN Chapter 3.4-3.7
2	Sept 4	Informed Search	RN Chapter 4.1-4.3	Paper1 Critique due
	Sept 6	Constraint Satisfaction	RN Chapter 5
3	Sept 11	Game	RN Chapter 6	IBM's Deep Blue Chess Grandmaster Chips
	Sept 13	Logic-based Reasoning	RN Chapter 7	HW1 due
4	Sept 18	First Order Logic	RN Chapter 8
	Sept 20	Inference in FOL	RN Chapter 9
5	Sept 25	Intro to Prolog	PP Chapter1-4,	A Short Prolog Tutorial, another Prolog Tutorial xsb and manual
	Sept 27	Prolog and NL Parsing	PP Chapter 9	Paper2 Critique due
6	Oct 2	Knowledge representation	RN Chapter 10
	Oct 4	Concept learning	RN Chapter 18.1-2, RN Chapter 19.1,
7	Oct 9	Concept learning (2)	ML Chapter 2	HW2 due
	Oct 11	Decision-tree learning	RN Chapter 18. 3, ML Chapter 3
8	Oct 16	Rule learning	ML Chapter 10.1-3	An Exercise on Entailment Part 1 due
	Oct 18	Uncertainty	RN Chapter 13	Paper3 Critique due
9	Oct 23	Naive Bayesian Classifier	ML Chapter 6.9-10
	Oct 25	Bayesian Network	RN Chapter 14.1-4	An Exercise on Entailment Part 2 due
10	Oct 30	No class
	Nov 1	Midterm Exam		HW3 due
11	Nov 6	NLP Application: LM		Project Proposal due
	Nov 8	NLP Application: POS
12	Nov 13	HMM		L. R Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of IEEE, 1989.
	Nov 15	Guest Lecture (Dr. Rong Jin)
13	Nov 20	HMM(2)
	Nov 22	Thanksgiving, no class
14	Nov 27	HMM(3)
	Nov 29	Recent Advances		HW4 due on Nov 29.
15	Dec 4	Final Project Presentation	Yun, Jeff, Brent, Ee-Foong, David, Andres
	Dec 6	Final Project Presentation	Eduardo, Charles, Ronald, Loretta, Daniel, Samah	Project Final Report due Dec 12

Links

Academic Honesty:

It is your responsibility to follow MSU's policy on academic integrity. Copying or paraphrasing someone's work (code included), or permitting your own work to be copied or paraphrased, even if only in part, is not allowed, and will result in an automatic grade of 0 for the entire assignment in which the copying or paraphrasing was done. Violation of academic integrity policy will result in a Grade F in the course.

Alternative Testing:

Alternative testing is available to those with a documented disability affecting performance on tests. Students with documented disabilities requiring some form of accommodation receive a Verified Individualized Services and Accommodations (VISA) document which displays verified testing accommodations when appropriate. Please visit Alternative Testing Guidelines if applied.

Notes: The instructor reserves the right to modify course policies and the course calendar according to the progress and needs of the class.