COSC 387: Artificial Intelligence

Syllabus, Fall 1998

Class Time: TR 11:40-12:55

Classroom: 502 Reiss

Web Page: http://www.cs.georgetown.edu/~maloof/cosc387/main.html

Instructor: Mark Maloof

Office: 527 Reiss

Mailbox: 225 Reiss

Phone: (202) 687-5034

Email: maloof@cs

Office Hours: TWR 1:00pm - 4:00pm (or by appointment)

Course Description

Artificial Intelligence (AI) is the branch of computer science that studies how to program computers to reason, learn, see, and understand. The lecture portion of this class will survey some of the basic concepts and techniques of artificial intelligence, including knowledge representations and inference mechanisms. Additional topics covered will be drawn from theorem proving, game playing, natural language understanding, machine learning, and computer vision. Applications of artificial intelligence will also be discussed and will include domains such as medicine, psychology, robotics, and computer security.

A semester research project will be required that is designed to provide depth in an area of interest to the student. In addition, there will be minor programming projects to emphasize key concepts. Students are free to use the programming language of their choice.

Prerequisites: COSC 173, or permission of instructor.

Primary Text: Artificial Intelligence: A Modern Approach, by Russell and Norvig.

Other Readings (on reserve at the science library):

Turing, A., Computing machinery and intelligence (read by 10/22).
Newell, A., and Simon, H.A., Computer science as empirical enquiry (read by 10/22).
Searle, J., Minds, brains, and programs (read by 12/8).
Dreyfus, H.L., and Dreyfus, S.E., Making a mind versus modeling the brain (read by 12/8).
Simon, H.A., Why should machines learn? (read by 12/8).

Other Resources:

Artificial Intelligence: a new synthesis, by Nilsson.
Artificial Intelligence: theory and practice, by Dean, Allen, and Aloimonos.
Artificial Intelligence, 3rd Edition, by Winston.

Schedule

Week Topic Chapters

1 (Sep 3) Background, Introduction
1

2 (Sep 8, 10) Introduction, Intelligent Agents
1, 2

3 (Sep 15, 17) Uninformed Search
3

4 (Sep 22, 24) Informed Search
4

5 (Sep 29, Oct 1) Game Playing, Logic
5, 6, 7

6 (Oct 6, 8) Resolution, Production Systems
9, 10

7 (Oct 13, 15) Production Systems, Planning
10, 11

8 (Oct 20, 22) Midterm, Uncertainty and Probability
15

9 (Oct 27, 29) Bayesian Inference, Decision Theory
15, 16

10 (Nov 3, 5) Machine Learning, Instance-based Learning
18.1, 18.2

11 (Nov 10, 12) Naive Bayes, Tree and Rule Induction
19.6, 18.3

12 (Nov 17, 19) Neural Networks, Genetic Algorithms
19, 20.8

13 (Nov 24) Machine Vision and Perception
24

14 (Dec 1, 3) Machine Vision and Perception
24

15 (Dec 8) Philosophical Issues
26

Mon, Dec 21, 4-6pm Final Exam

Grading

Midterm (Oct 20)	20%
Research Project (due Dec 8)	35%
Final (Mon, Dec 21, 4-6pm)	25%
Programming Projects	10%
Participation, Quizzes, etc.	10%

Class Time:	TR 11:40-12:55
Classroom:	502 Reiss
Web Page:	http://www.cs.georgetown.edu/~maloof/cosc387/main.html

Instructor:	Mark Maloof
Office:	527 Reiss
Mailbox:	225 Reiss
Phone:	(202) 687-5034
Email:	maloof@cs
Office Hours:	TWR 1:00pm - 4:00pm (or by appointment)

Week	Topic	Chapters

1 (Sep 3)	Background, Introduction	1
2 (Sep 8, 10)	Introduction, Intelligent Agents	1, 2
3 (Sep 15, 17)	Uninformed Search	3
4 (Sep 22, 24)	Informed Search	4
5 (Sep 29, Oct 1)	Game Playing, Logic	5, 6, 7
6 (Oct 6, 8)	Resolution, Production Systems	9, 10
7 (Oct 13, 15)	Production Systems, Planning	10, 11
8 (Oct 20, 22)	Midterm, Uncertainty and Probability	15
9 (Oct 27, 29)	Bayesian Inference, Decision Theory	15, 16
10 (Nov 3, 5)	Machine Learning, Instance-based Learning	18.1, 18.2
11 (Nov 10, 12)	Naive Bayes, Tree and Rule Induction	19.6, 18.3
12 (Nov 17, 19)	Neural Networks, Genetic Algorithms	19, 20.8
13 (Nov 24)	Machine Vision and Perception	24
14 (Dec 1, 3)	Machine Vision and Perception	24
15 (Dec 8)	Philosophical Issues	26
Mon, Dec 21, 4-6pm	Final Exam