Classroom: 203-G, 7:15-8:55 p.m., MW

Instructor: Dr. Yanqing Zhang

Office: 776 College of Education Building

Phone: 404-651-0682 (o)

Fax: 404-651-2246

E-mail: yzhang@cs.gsu.edu

Website: http://www.cs.gsu.edu/~matyqz/courses/fall2001/ai/ai2001fallweb.html

Office Hours: 2:30 – 4:00 p.m. MW or by appointment

Text: Artificial Intelligence - A Modern Approach , by S. J. Russell and P. Norvig, Prentice Hall, 1995.

Course Content: Introduction to basic AI techniques and methodologies. Topics include search strategies, problem solving, knowledge and reasoning, logic and deduction, expert systems, neural networks, fuzzy logic, genetic algorithms, learning, data mining, intelligent agents, etc. Hands-on programming projects.

Prerequisite: CSc 3410 Data Structures and CSc 4330 Programming Language Concepts.

Course Requirements: All students should not only learn basic theoretical principles but also accumulate practical hands-on experience. Undergraduate students and graduate students will do assignments, take tests and finish programming projects. Team work is encouraged. At the end of this semester, all graduate students and undergraduate students will give presentations to share knowledge and skills. An undergraduate student needs to write a technical report. A graduate student needs to write a conference paper.

Class Policy:

Ø Attendance: Students are required to attend all classes.

Ø Academic honesty: Plagiarism will result in a score of zero on the test or paper. The instructor has the right to make a decision of if two or more papers are cheating.

Ø Assignments and Projects: They must be handed in on time and will not be accepted when past due.

Ø Withdrawals: Oct. 12 Friday is the last day to withdraw and possibly receive a W.

Ø Make-ups: Must need the instructor's special permission.

Grading Policy:

Mid-term Exam 25%		A [90, 100]
Final Exam 25%		B [80, 90)
Assignments 15%		C [70, 80)
Projects 30%		D [60, 70)
Attendance 5%		F [0, 60)

Tentative Course Outline and Schedule:

Chapter 1 Introduction	Aug. 20, 22
Chapter 2 Intelligent Agents	Aug. 27, 29
Chapter 3 Solving Problems by Searching	Sept. 5, 10, 12
Chapter 4 Informed Search Methods	Sept. 17, 19
Chapter 5 Game Playing	Sept. 24, 26
Chapter 6 Agents that Reason Logically	Oct. 1, 3
* Mid-term Exam	Oct. 9
Chapter 14 Uncertainty	Oct. 10, 15, 17
Chapter 15 Probabilistic Reasoning Systems	Oct. 22, 24
Chapter 18 Learning from Observations	Oct. 29, 31
Chapter 19 Learning in Neural and Belief Networks	Nov. 5, 7
Chapter 20 Reinforcement Learning	Nov. 12, 14
Chapter 27 AI: Present and Future	Nov. 19
# Project Presentations	Nov. 26, 28, Dec. 3
* Final Exam (Project Due)	Dec. 5 7:15-8:55 p.m. W

Statement: This course syllabus provides a general plan for the course; deviations may be necessary.