Class Page - CPS 841 Fall Semester 2001

This is an introduction to Artificial Intelligence for graduate students. It is intended as a survey in the many aspects of AI and will concentrate on the basic concepts of representation and control, followed by some (limited) examples of the use of these concepts in important AI subfields (Natural Language, Learning, Expert Systems, Robotics). As such, this course will not do much in-depth study of any one area. Rather this course is intended as a good introduction for those who have had no exposure to AI and as a stepping stone for those interested in specific areas of AI. We will try to cover a significant portion of the book during the semester. We will also read a number of papers from the literature to reinforce concepts introduced in class.

Rather than present AI as a loose collection of ideas and techniques, this course will strive to emphasize important unifying themes that occur throughout many areas of AI research. These include:

Many methods can be viewed as searching through a space of hypotheses
Search can be made more effective through prior knowledge
The viewpoint of an intelligent "agent" as an effective one for describing and understanding AI approaches.
The new viewport of "mental development" potentially a unifying framework for all the subareas of AI.

Instructor: John Weng

Email: weng@cse.msu.edu

Meeting Times: MWF 9:10 - 10:20 AM

Classroom: 2150 EB

Office Hours (in 2325 EB)

Wednesdays 5:00pm - 6:00pm

Thursdays 5:00pm - 6:00pm

Teaching Assistant: Robert A Post, Jr

Email: postrobe@cse.msu.edu

Office Hours (tentative)

Thursdays 12:30pm -1:30pm

Text: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, Prentice-Hall 1995

Course Prerequsites:

Programming skills in a high level language (C, C++, Lisp)
Exposure to concepts in discrete structures, probability/statistics, and algorithmic analysis