CS 430/530: Artificial Intelligence
Fall 2007
Syllabus

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

Computer Science

Wissink Hall 231

Phone: 507-389-5587

Fax: 507-389-6376

Email: bates@mnsu.edu

 


Course Website

http://bates.cs.mnsu.edu/cs430
Check the website regularly for announcements and updates. 


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**

*will be in WH 119
**will be held in the ACC or WH231.  See class notes for canceled days.

If things that are useful for the entire class come up, they will be posted on the announcement section of the class webpage so please check it regularly.


Course Materials

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

 

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 and/or Statistical Models (~ 2 wks)

Grading Distribution
Homework and Lab work: 25-35%
2 Midterm Exams & Final: 65-75%        

Important Dates
Midterm 1: TBA (early-October)
Midterm 2: TBA (mid-November)
Final: Tuesday, December 11, 12:30-2:30pm

Course Tools

Homework and Exams
    The homework for this course will include problems from the book as well as programming and small research projects.

    Your exams will be based on information gained through both homework and project experience as well as material covered in lectures and the book. 

 

Expectations of Students

Disabilities
Students who may need accommodations for a disability can make an appointment to see me during my office hours to discuss your needs.


Academic Honesty

By staying enrolled in this class, you agree to abide by the University's Policy for Academic Honesty which appears in the Student Handbook under the section heading "Academic Honesty". If you have questions about the policy please contact me, your advisor, or another faculty member PRIOR to engaging in a "dishonest" act. Failure to abide and respect the Academic Honesty Policy will result in severe penalties as allowed by the University.