CSCI 681(323):  Theory of Artificial Intelligence

Registration/Schedule Information

Term/Year	TRACS Call#	Section	Act	Days	Time	Room	Instructor(s)
Spring 2005	14413	CSCI 323-01	DIS	M W	9:00-10:50 9:00-9:50	OCNL 244     OCNL 244/247	Juliano

Prerequisites

CSCI 584 (CSCI 223), Artificial Intelligence and classified graduate standing.

Description

3 units.  An in-depth study of current techniques, applications, and issues in artificial intelligence. Suitable topics include advanced knowledge representation; natural language understanding; machine learning; theory of functional programming; cognitive science; neural networks; philosophy and artificial intelligence.

Required Accounts

	Students officially registered for the course will have their own Chico State Connection (CSC Portal) account.
	Students are responsible for regularly checking their WebCT account (automatically generated through the CSC Portal) to access an up-to-date on-line calendar of events, current scores, on-line quizzes, etc.

Required Text(s)

Artificial Intelligence: A Modern Approach, 2/e Stuart Russell and Peter Norvig, 2003. Prentice Hall, Upper Saddle River, NJ. ISBN 0-13-790395-2.

Potential Supplementary Text(s)

Fuzzy Logic for Embedded Systems Applications (e-book) A. Ibrahim, 2004. Elsevier/Newnes, Burlington, MA. ISBN 0-7506-7605-1.

Course Objectives

The objectives of this course are to:

1. provide graduate students with sufficient background required to tackle specialized AI courses (e.g., Machine Learning, Natural Language Processing, etc.) and/or to facilitate graduate research in the area of intelligent systems;
2. provide graduate students the opportunity to learn to read and present scientific/technical texts from recent literature on intelligent systems; and
3. provide graduate students with the basic framework for further/advanced study, research, and application of intelligent systems techniques.

Course Outcomes

Upon successful completion of this course, the student shall be able to:

1. apply learned fundamentals of intelligent systems to tackle specialized AI courses and graduate research topics;
2. apply learned understanding of current scientific/technical findings in intelligent systems; and
3. pursue further/advanced study, research, and application of intelligent systems techniques.

Grade Evaluation

This is a graduate-level, seminar-oriented course. Topical coverage is typically based on continuing from CSCI 584 (CSCI 223) coverage, which includes: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning. Students are expected to read the material in advance and to lead discussions of various topics covered. Students should be familiar with uninformed search algorithms (depth-first and breadth-first methods), discrete probability (random variables, expectation, simple counting), propositional logic (boolean algebra), basic algorithms and data structures, basic computational complexity, and basic calculus. The course is designed to give students an equal opportunity of exposure to both Theory and Practice. Students are expected to demonstrate proficiency on both the theoretical and practical aspects of this course.

Theoretical Component  (50%)
	40%		Midterm Exam
	60%		Final Exam (as scheduled in the Class Schedule)
Practical Component  (50%)
	100%		Participation in class discussions
				* may include at least one research paper
				* possible peer review/evaluation of research papers
				* possible written homework

Students are required to earn a C- (70%) or better in both the Theoretical and the Practical components; otherwise, the minimum of the scores of the two components will be used to calculate the student's final grade.

Final Grades

Final grades shall be expressed as a percentage of the maximum possible score of all evaluated materials. Letter grades will be given according to the University definition of letter grading symbols (please refer to the University Catalog for detailed information).

Tentative Schedule

The following tentative schedule is subject to change without notice:

Week	Chapter	Coverage/Comments
1	1	Introduction, background material;     review of material from CSCI 584 (CSCI 223)
2	7	Logical agent (review)
3	8 9	First-order logic (review)
4	10	Knowledge representation (review)
5	11	Planning
6	12	Planning and acting in the real world
7	13	Uncertainty     Midterm Exam, class time
8	13 14a/b	Uncertainty     Probabilistic reasoning
9	15a/b	Probabilistic reasoning over time
10	16	Making simple decisions
11	17	Making complex decisions
12	18	Learning from observations
13	19	Knowledge in learning
14	20	Statistical learning methods
15	21	Reinforcement learning
16		Final Exam, as scheduled (see Class Schedule)