CS 486/686: Introduction to Artificial Intelligence (Spring 25)

Instructor: Yuntian Deng

Instructor Email: yuntian@uwaterloo.ca

Course Piazza: https://piazza.com/uwaterloo.ca/spring2025/cs486686

Office Hours: Thursdays 4-5pm in DC2633

Course Schedule:

• Section 001: Tuesday/Thursday 2:30PM - 3:50PM in MC 4045
• Section 002: Tuesday/Thursday 11:30AM - 12:50PM in MC 2054

First Class: May 6, 2025

Final Exam: Aug 13, 2025

Project Proposal Due: July 3, 2025

Reduced TA office hours: We've reduced some TA office hours due to their additional assignment responsibilities.

Note: This is a provisional version of the syllabus. Expect changes over time.

Teaching Assistants

All TA emails end with @uwaterloo.ca

• Wentao Zhang (w564zhan) - Piazza Management
• Hala Sheta (hsheta) - Quiz Management
• Ruoxi Ning (r2ning) - Assignment 1 Management
• Shuhui Zhu (s223zhu) - Assignment 2 Management
• Bihui Jin (b27jin) - Assignment 3 Management
• Haolin Yu (h89yu) - Office Hours

Important: For inquiries related to a particular duty (e.g., Piazza, quizzes), please directly reach out to the corresponding TA and cc the instructor. The instructor receives many emails and response time may be slow.

Office Hours

• Yuntian Deng: Thursday 4-5pm in DC2633
• Haolin Yu: Friday 4-5pm in DC 2633

Communication

• All communication should take place using the Piazza discussion board.
• We do not upload materials or assignments to Piazza; these materials will appear on LEARN.
• Public Piazza posts (can be anonymous) are the preferred method for questions about course material. Students can then help each other and instructors can read/reply.
• Private Piazza posts (to instructors only) can be used for any posts that contain solution snippets or private questions.
• Only in exceptional cases where you need to contact only the instructor should you use the personal email above.

Waitlist

If you cannot register for the course, please send an email to yuntian@uwaterloo.ca to join the waitlist.

Course Description

This course provides an introduction to the field of artificial intelligence. Topics include search algorithms, game playing, knowledge representation and reasoning, uncertainty and probabilistic reasoning, machine learning, neural networks, and reinforcement learning.

Assessment

Project (CS686)

• The project is meant to be finished individually.
• The project proposal deadline is July 1, 2025.
• The project can consist of a new AI algorithm, a new theoretical analysis of an existing AI algorithm, a new dataset or benchmark to evaluate existing AI algorithms, an empirical evaluation of existing AI algorithms, or a literature survey.
• The proposal should contain a 1-page description about the problem you aim to solve, including background, motivation, and proposed methods.
• The project needs to be related to the course, including search algorithms, hidden Markov models, reinforcement learning, neural networks.
• The project needs to contain the following sections: problem definition, dataset construction, algorithm design, experiments, evaluation, conclusion.
• Please use LaTeX template to write the final report.
• Please make sure the code is not copied from public repositories. Any violation will be seen as plagiarism with serious consequences.

Submission Guidelines

• Assignments are to be done individually unless otherwise stated.
• Submit assignments and receive marks through Learn.
• No late assignments will be accepted.
• Submit project proposals on LEARN before the deadline.
• Students wishing to write a project (and all CS686 students) must submit a project proposal.

Reading Materials

Primary Text:

David Poole and Alan Mackworth "Artificial Intelligence: Foundations of Computational Agents". Cambridge University Press, (2nd edition: 2017).

(Available online. Check the online resources and in particular the Python programs.)

Secondary Readings:

• Russell and Norvig "Artificial Intelligence: A Modern Approach"
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville "Deep Learning"
• Richard Sutton and Andrew Barto "Reinforcement Learning: An Introduction"

Course Schedule

The exact schedule will be updated as the course progresses.

Date	Lecture	Topic	Notes
Search
Tue, May 6	L1	Introduction to Artificial Intelligence	Slides
Thu, May 8	L2	Uninformed Search	Slides
Tue, May 13	L3	Heuristic Search	Slides, Quiz 1 (due Tue, May 27)
Thu, May 15	L4	Heuristic Search (continued)	Slides
Tue, May 20	L5	Constraint Satisfaction Problems	Slides
Uncertainty Estimation
Thu, May 22	L6	Local Search	Slides, Quiz 2 (due Tue, May 27)
Tue, May 27	L7	Probabilities	Slides, Quiz 1 due, Quiz 2 due
Thu, May 29	L8	Independence and Bayesian Networks I	Slides, Quiz 3 (due Tue, Jun 3), Assignment 1 Release (due Thur, Jun 12)
Tue, Jun 3	L9	Independence and Bayesian Networks II	Slides, Quiz 3 due
Thu, Jun 5	L10	Variable Elimination Algorithm	Slides, Quiz 4 (due Tue, Jun 10)
Tue, Jun 10	L11	Hidden Markov Models I	Slides, Quiz 4 due
Thu, Jun 12	L12	Hidden Markov Models II	Slides, Quiz 5 (due Tue, Jun 17), Assignment 1 Due
Markov Decision Process
Tue, Jun 17	L13	Decision Theory	Slides, Quiz 5 due
Thu, Jun 19	L14	Markov Decision Processes I	Slides, Quiz 6 (due Tue, Jun 24), Assignment 2 Release
Tue, Jun 24	L15	Markov Decision Processes II	Slides, Quiz 6 due
Thu, Jun 26	L16	Reinforcement Learning	Slides, Quiz 7 (due Tue, Jul 1)
Machine Learning & Deep Learning
Tue, Jul 1		No Class
Thu, Jul 3	L17	Introduction to Machine Learning	Slides, Quiz 8 (due Tue, Jul 8), Quiz 7 due, Project Proposal Due (CS686)
Tue, Jul 8	L18	Unsupervised Learning	Slides, Assignment 2 Due, Quiz 8 due, Assignment 3 Release
Thu, Jul 10	L19	Decision Trees	Slides, Quiz 9 (due Tue, Jul 15)
Tue, Jul 15	L20	Neural Networks I	Slides, Quiz 9 due
Thu, Jul 17	L21	Neural Networks II	Slides, Quiz 10 (due Tue, Jul 22)
Tue, Jul 22	L22	Neural Networks III	Slides, Quiz 10 due, Assignment 3 Due
Thu, Jul 24	L23	Course Recap	Slides
Thu, Jul 24	Final Project Report Due (CS686)
Wed, Aug 13	Final Exam, 12:30PM - 3PM, PAC UPPER 9, 10

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