CS 463G Artificial Intelligence Syllabus

Class page

Time and Place: 10:00 am - 10:50 am, 263 FPAT

Professor: Dr. J. Goldsmith
Office: 311 Marksbury Building, Phone: 257-4245 (email is more reliable)
Office Hours: TBA and by appointment. Email questions strongly encouraged and answered.

Course Description:

The topics covered in this course will be:

intelligent agents
search and game-playing
logical systems
planning systems
uncertainty---probability theory, inference, and decision theory

The course will cover both theory and practice, including programming assignments that utilize concepts covered in lectures.

Prereqs: CS 315 and CS 375, and engineering or graduate standing. You should know how to program, be familiar with basic algorithms and data structures, especially for graphs and trees, and be familiar with propositional and predicate logic.

Textbook: Artificial Intelligence: A Modern Approach, 3rd Edition by Stuart Russell and Peter Norvig. Prentice Hall, 2009.

Grading:

There will be assignments approximately every other week, due Fridays at the beginning of class. Assignments will be posted on the web two weeks prior to the due date. The lowest homework grade will be dropped. Illegible work will not be graded. Plagiarized work will be penalized for all parties, according to University regulations. There will be one midterm and one final. In addition, you will be expected to post occasionally to an online forum, and to answer questions on that forum.

Assignments (problems and programs) will be 60% of your grade, postings will be 8%, the midterm project will be 15% and the final 17%. The midterm project will be completed by October 15th; a project proposal is due September 15th. The final project is due by Tuesday, Dec. 16th at 1 PM, although any in-class presentations must be scheduled during a class period. The final project proposal is due Monday, November 10.

Those taking CS 463G for graduate credit (in any department except CS) will have one additional assignment of a paper or presentation.

READ THIS:

Attendance in class and section is very strongly encouraged.

Copying of homework from other students or from other sources is strictly prohibited. Obtaining a solution from another source without citing the source is plagiarism. You are encouraged to visit Dr. Goldsmith or your T.A. in their office hours or to send them email if you are stuck on homework problems. You do not need an appointment for regularly scheduled hours.

Outcomes and assessments

The following are the stated learning outcomes for this course. These will be assessed by a survey at the end of the semester, in compliance with certification standards for academic Computer Science departments.

Students will learn basic concepts in logic and artificial intelligence. In particular, the student will be able to:

use current techniques, skills, and tools necessary for computing practices;
apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices;
understand search algorithms;
analyze games;
use logic to represent and reason about domain knowledge;
use Bayesian networks to represent and reason about domain knowledge;
model and solve planning problems;
understand the relevance of Artificial Intelligence to the real world;
program in a declarative programming language;
improve her/his ability to analyze a problem, and identify and define the computing requirements appropriate to its solution;
improve her/his ability to design, implement and evaluate a computer-based system, process, component, or program to meet desired needs.

Week by Week Course Outline:

Date Topic Chapter Assignment
INTRODUCTION
Aug. 27-9 Intro to course, AI, agents 1
SEARCH
Sept. 3-5 Agents, uninformed search 2,3 Puzzle, part 1
Sept. 8-12 Informed search 3,4 Puzzle, part 2
Sept. 15 MIDTERM PROJECT PROPOSAL DUE
Sept. 15-19 Search heuristics; intro to SAT
Sept. 22-26 SAT algorithms, constraint satisfaction
Sept. 29-Oct. 3 Games 5 SAT programs
LOGICAL SYSTEMS
Oct. 6-10 Logic, reasoning, proofs 7, 8
Oct. 13-17 Wumpus world, situation calculus 7,8
Oct. 15 MIDTERM PROJECT DUE
Oct. 20-24 Inference, resolution, Prolog 9 First Prolog program
PLANNING AND UNCERTAINTY
Oct. 27-31 Planning 11 Second Prolog program
Nov. 3-7 Uncertainty: Probability theory, Na\"ive Bayes classifiers 13
Nov. 10 FINAL PROJECT PROPOSAL DUE
Nov. 10-14 Bayesian networks 14-17 MCMC Program
Nov. 17-21 Planning under uncertainty

Nov. 24 Preferences

Nov. 26-28 THANKSGIVING BREAK (Class does not meet)

Dec. 1-5 Machine learning 18 MDP/RL program
Stanford ML lectures
Dec. 8-12 Topics in AI 20
Dec. 16 FINAL PROJECT DUE: 1 PM

Date	Topic	Chapter	Assignment
	INTRODUCTION
Aug. 27-9	Intro to course, AI, agents	1
	SEARCH
Sept. 3-5	Agents, uninformed search	2,3	Puzzle, part 1
Sept. 8-12	Informed search	3,4	Puzzle, part 2
Sept. 15	MIDTERM PROJECT PROPOSAL DUE
Sept. 15-19	Search heuristics; intro to SAT
Sept. 22-26	SAT algorithms, constraint satisfaction
Sept. 29-Oct. 3	Games	5	SAT programs
	LOGICAL SYSTEMS
Oct. 6-10	Logic, reasoning, proofs	7, 8
Oct. 13-17	Wumpus world, situation calculus	7,8
Oct. 15	MIDTERM PROJECT DUE
Oct. 20-24	Inference, resolution, Prolog	9	First Prolog program
	PLANNING AND UNCERTAINTY
Oct. 27-31	Planning	11	Second Prolog program
Nov. 3-7	Uncertainty: Probability theory, Na\"ive Bayes classifiers	13
Nov. 10	FINAL PROJECT PROPOSAL DUE
Nov. 10-14	Bayesian networks	14-17	MCMC Program
Nov. 17-21	Planning under uncertainty

Nov. 24	Preferences

Nov. 26-28	THANKSGIVING BREAK (Class does not meet)

Dec. 1-5	Machine learning	18	MDP/RL program
	Stanford ML lectures
Dec. 8-12	Topics in AI	20
Dec. 16	FINAL PROJECT DUE: 1 PM

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This page last modified: MOnday, July 27, 2009.