CS 430/530: Artificial Intelligence

Announcements:  Check here regularly for announcements!

Welcome to CS 430! 

Previous Announcements

Course description: This course offers an overview of the field of Artificial Intelligence (AI). The course covers basic introductory concepts and provides a history of the field. Emphasis is placed on the knowledge representation and reasoning strategies used for AI problem solving. Students learn to implement problem solutions using the LISP programming language. Additional advanced AI topics are covered as time permits.

Pre-requisites: CS 210 (Data Structures so COMS 310 is the equivalent) or CS 230

Professor: Rebecca Bates (bates@mnsu.edu) 
Contact Information

Course Hours and Location

Lectures TW 1-2pm	WH 286A
Th 1-2pm	WH 284

Office Hours

Monday	Tuesday	Wednesday	Thursday	Friday
by appointment	2-2:30, 4:30-5:30	11-12*, 2-3*	2-2:30	10-12**

* in WH 119
** in ACC or WH 231

Prof. Bates will also read and respond to email questions within 24 hours.  You can usually expect responses to questions the same day if received prior to 10pm. If things that are useful for the entire class come up, they will be posted on the announcement section of the class webpage so check it regularly.

Course Materials
Required Text: Artificial Intelligence A Modern Approach, Second Edition, Stuart Russell & Peter Norvig, 2003.

Course Syllabus

Student Outcomes

Students who complete this course will be able to:

1. Describe basic history of Artificial Intelligence (AI).

2. Recognize and define basic AI terminology.

3. Work with propositional and predicate logic.

4. Use production rules and forward or backward chaining to solve problems.

5. Represent “knowledge” in several data structures.

6. Implement the A* algorithm.

7. Describe several heuristic search techniques.

8. Write a LISP program.

Tentative Topic List

1. Intelligent Agents (~ 2 wks)

2. Problem Solving Through Search (~ 2 wks)

3. Games as Search Problems (~ 2 wks)

4. Reasoning and Logic (~ 2 wks)

5. Knowledge and Reasoning (~ 2 wks)

6. Learning from Observation (~ 2 wks)

7. Neural Networks (~ 2 wks)

Grading
Homework and Lab work: 35%
2 Midterm Exams: 40%        
Final exam: 25%

Important Dates
Midterm 1: TBA
Midterm 2: TBA
Final: Tuesday, December 11, 12:30-2:30pm

Course Tools

• HPC & Krypton: You will get an account on both of these machines.  If you haven't set up a krypton account, please do so since that will be the portal to the HPC (High Performance Computer).  You can set up the account through the mavmail/mavdisk account and password pages.
• Desire2Learn: This web-based software package will be used for turning in programming assignments and feedback surveys.
• Password protected webpages: The Computer Science Lab computers host these pages.  The assignments and solutions will be available on the web but will be password protected. 

Other Information

CS 430 Handouts and Assignments

Additional Resources

Here is a listing of useful AI URLs (thanks to Prof. Roiger). The reference is listed below.

• http://aaai.org

• http://www1.cs.columbia.edu/~acl/home.html

• www.auai.org

• www.auai.org

• www.cognitivesciencesociety.org

• www.cra.org

• http://cscsi.sfu.ca

• www.flairs.com

• www.iasted.org

• http://ieee-nns.org

• http://ijcai.org

• www.inns.org

• http://isai.cs.swt.edu

• http://www.acm.org/sigart

• ww.acm.org/sigkdd

• www.cs.uvm.edu/%7exwu/tfvi/index.shtml

• http://tab.computer.org/pamitc

• www.kdnuggets.com

Majority taken from: Hamilton, M., Mitchell T., and Hamilton, C. (2003). AI Matures and Flourishes in North America. IEEE Intelligent Systems, July/August, 87-89.

Page last modified by R.A. Bates on 07/28/2008 05:24 PM.