---

Overview • Schedule • References • Office Hours • Applied AI Project

Overview

Theoretical and applied aspects of artificial intelligence. Course topics include problem solving and search, knowledge representation and reasoning, decision-making under uncertainty, machine learning, and multi-agent systems. Recommended Background: Programming, data structures and algorithms, linear algebra, probability theory.

Schedule

Note: This schedule is subject to change.

Week	Date	Topic	Notes
1	Aug 19	Introduction	P1 Released
2	Aug 24	Informed Search
	Aug 26	Uninformed Search
3	Aug 31	Constraint Satisfaction
	Sept 2	No Class	P1 Due
4	Sept 7	Iterative Improvement
	Sept 9	Adversarial Search	P2 Released
5	Sept 14	Machine Learning I
	Sept 16	Machine Learning II
6	Sept 21	Perceptron
	Sept 23	Virtual Job Fair
7	Sept 28	Neural Networks	P2 Due
	Sept 30	Fall Break
8	Oct 5	Midterm Exam
	Oct 7	Midterm Exam
9	Oct 12	Model Evaluation
	Oct 14	Model Engineering	P3 Released
10	Oct 19	Engineer's Day
	Oct 21	Cancelled
11	Oct 26	Decisions and Initiative
	Oct 28	Bayes Nets
12	Nov 2	Hidden Markov Models	P3 Due
		Guest Lecturer: Zhixiu Lu
	Nov 4	Adversarial Attacks
		Guest Lecturer: Zhuohang Li
13	Nov 9	Reinforcement Learning
	Nov 11	Search Engines	P4 Released
14	Nov 16	Interface User Interfaces
	Nov 18	AI and Creativity
		Guest Lecturer: Rhema Linder
15	Nov 23	Thanksgiving Week
	Nov 25	Thanksgiving Week
16	Nov 30	Applied AI Project Time	P4 Due
	-
17	Dec 9	Final Exam	CS523: Project Due

References

This course will make periodic reference to the followingreferences

Textbook References

• [DP] D. Poole and A. Mackworth. Artificial Intelligence: Foundations of Computational Agents. 2nd Ed, 2017
• [DP] S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. 4th Ed, 2020
• [RS] R. Sutton and A. Barto. Reinforcement Learning: An Introduction. 2018.
• [IG] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. 2016.