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: To be announced
Waitlist Update: I have requested all students who requested to join the waitlist to be added to the course. You should receive further instructions from the graduate office in a few days. In case I missed any student, if you haven't received any information by this Friday, please email me.
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
- Bihui Jin (b27jin)
- Ruoxi Ning (r2ning)
- Shuhui Zhu (s223zhu)
- Haolin Yu (h89yu)
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
- Bihui Jin: Monday 1-2pm in DC 2633
- Shuhui Zhu: Tuesday 1-2pm in DC 2633
- Yuntian Deng: Thursday 4-5pm in DC2633
- Ruoxi Ning: Thursday 6-7pm in DC2305
- 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.
Waitlist Update: I have requested all students who requested to join the waitlist to be added to the course. You should receive further instructions from the graduate office in a few days. In case I missed any student, if you haven't received any information by this Friday, please email me.
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
For CS486 students:
- 3 Assignments (30% - to be done individually - dates to be announced)
- 10 weekly after-class quizzes (20% - to be announced weekly)
- Two and a half hour written final examination (50% and must pass the final to pass the course)
For CS686 (grad) students:
- 3 Assignments (30% - to be done individually - dates to be announced)
- Project done individually (30%)
- Two and a half hour written final examination (40%)
Note about Quizzes: All quizzes are due two days after release date in LEARN, at 11:59pm.
Project (CS686)
- The project is meant to be finished individually.
- The project proposal deadline will be announced.
- 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 | Constraint Satisfaction Problems | Quiz 1 due |
| Tue, May 20 | L5 | Local Search | Quiz 2 (due Thu, May 22) |
| Uncertainty Estimation | |||
| Thu, May 22 | L6 | Probabilities | Quiz 2 due |
| Tue, May 27 | L7 | Independence and Bayesian Networks I | Quiz 3 (due Thu, May 29), Assignment 1 Release |
| Thu, May 29 | L8 | Independence and Bayesian Networks II | Quiz 3 due |
| Tue, Jun 3 | L9 | Variable Elimination Algorithm | Quiz 4 (due Thu, Jun 5) |
| Thu, Jun 5 | L10 | Hidden Markov Models I | Quiz 4 due |
| Tue, Jun 10 | L11 | Hidden Markov Models II | Quiz 5 (due Thu, Jun 12), Assignment 1 Due |
| Markov Decision Process | |||
| Thu, Jun 12 | L12 | Decision Theory | Quiz 5 due |
| Tue, Jun 17 | L13 | Markov Decision Processes I | Quiz 6 (due Thu, Jun 19) |
| Thu, Jun 19 | L14 | Markov Decision Processes II | Quiz 6 due, Assignment 2 Release |
| Tue, Jun 24 | L15 | Reinforcement Learning | Quiz 7 (due Thu, Jun 26) |
| Machine Learning & Deep Learning | |||
| Thu, Jun 26 | L16 | Introduction to Machine Learning | Quiz 7 due |
| Tue, Jul 1 | L17 | Unsupervised Learning | Quiz 8 (due Thu, Jul 3), Project Proposal Due (CS686) |
| Thu, Jul 3 | L18 | Decision Trees | Quiz 8 due, Assignment 2 Due |
| Neural Networks | |||
| Tue, Jul 8 | L19 | Neural Networks I | Quiz 9 (due Thu, Jul 10), Assignment 3 Release |
| Thu, Jul 10 | L20 | Neural Networks II | Quiz 9 due |
| Tue, Jul 15 | L21 | Neural Networks III | Quiz 10 (due Thu, Jul 17) |
| Thu, Jul 17 | Additional Topics | Quiz 10 due | |
| Tue, Jul 22 | Project Presentations (CS686) | Assignment 3 Due | |
| Thu, Jul 24 | L23 | Course Recap | |
| TBA | Final Exam | ||
| TBA | Final Project Report Due (CS686) | ||
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