CS480 Artificial Intelligence

Course Description

Introduction to computational methods for intelligent control of autonomous agents, and the use of programming paradigms that support development of flexible and reactive systems. These include heuristic search, knowledge representation, constraint satisfaction, probabilistic reasoning, decision-theoretic control, and sensor interpretation. Particular focus will be places on real-world application of the material. Prerequisites: CS 331, and CS 401, or CS 403.

Course Topics

The following is a tentative and partial list of topics that will be covered in the class:

Artificial intelligence overview
Problem solving - searching and constraint satisfaction
Knowledge representation - first-order logic, uncertainty representation
Reasoning - Inference in first-order logic, probabilistic reasoning
Learning - learning from examples, reinforcement learning

Course Information

Time and Location: Mon - Wed 1:50 - 3:05pm in Stuart 238
Professor: Mustafa Bilgic
Office: Stuart 228C
Email: mbilgic AT iit.edu
Office Hours: Mon - Wed 11am - 12pm (Other times by appointment)

Prerequisites

CS 331, and CS 401, or CS 403.

Course Format and Grading

In my couse, the slides often serve only as a guide; I use the white board heavily.

The evaluation will consit of written assignments (~six), programming assignments (~three), a midterm, and a final. The point breakdown is:

Written assignments: 15%
Programming assignments: 15%
Midterm: 30%
Final: 40%

Late submission policy: You have seven free late days without penalty. You can use those days in any way you like for the written and/or programming assignments; you can use all of them for a single assignment or use them sparingly. However, once you run out of free days, late submissions will not be accepted. Absolutely positively no exceptions. So, use them wisely.
Collaboration policy: You can discuss the written assignments with your friends; however, everyone has to write their own solutions in their own words.
Code of academic honesty: Please read the procedures on academic honesty here. If you violate the academic honesty (such as unauthorized collaboration, cheating, etc.), then depending on the severity of the violation, it can result in i) getting zero points on the respective assignment, ii) expulsion from the course, iii) suspension of your enrollment at the university, iv) expulsion from the university.

Course Material

There is a required text book for this course:

Artificial Intelligence: A Modern Approach, 3rd edition, by Stuart Russell and Peter Norvig

There will be additional reading materials (mostly available on the web).

Tentative Schedule

Date	Topic	Required Reading
Aug 22	Introduction and Course Overview	ch. 1
Aug 24	Intelligent Agents & Problem Solving	ch. 2
Aug 29	Search	ch. 3
Aug 31	Search
Sep 5	LABOR DAY - NO CLASS
Sep 7	Game Playing	ch. 5
Sep 12	Game Playing
Sep 14	Constraint Satisfaction Problems (CSPs)	ch. 6
Sep 19	CSPs and Constraint Propagation
Sep 21	KR & Logical Agents	ch. 7
Sep 26	Resolution & Theorem Proving
Sep 28	First Order Logic (FOL)	ch. 8
Oct 3	FOL & Theorem Proving	ch. 9
Oct 5	Review
Oct 12	MIDTERM (TENTATIVE!)
Oct 17	Planning	ch. 11
Oct 19	Planning	ch. 12
Oct 24	Uncertainty	ch. 13
Oct 26	Probabilistic Reasoning	ch. 14
Oct 31	Probabilistic Reasoning
Nov 2	Probabilistic Reasoning	ch. 15
Nov 7	Decision Making Under Uncertainty	ch. 16
Nov 9	Decision Making Under Uncertainty
Nov 14	Decision Making Under Uncertainty	ch. 17
Nov 16	Learning	ch. 18
Nov 21	Learning	ch. 20
Nov 23	THANKSGIVING HOLIDAY - NO CLASS
Nov 28	Reinforcement Learning	ch. 21
Nov 30	Wrap-up
TBA	FINAL