CS486/686: Introduction to Artificial Intelligence
Spring 2024
People
- Instructor:
- TAs (email should end with @uwaterloo.ca):
- Dhanraj, Varun (vdhanraj)
- Hu, Theo (z97hu)
- Jafari, Aref (a22jafar)
- Luo, Yudong (y264luo)
- Mokhtari, Sabrina (s4mokhta)
- Xiong, Rui Ming (rmxiong)
- Yang, Jheng-Hong (j587yang)
- Zhang, Dake (d346zhang)
- Zhu, Shuhui (s223zhu)
WaitList
If you cannot register the course, please fill out the
Google form to join the waitlist.
Communication
- All communication should take place using the Piazza discussion board.
- We do not upload materials or assignments to Piazza, instead, these materials will appear on LEARN
- Sign up for Piazza (if you're not already) here.
- Public Piazza posts (can be anonymous) are the preferred method for questions about course material, etc. 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.
Deliverables
- Assignments has both written part and coding part.
- The assignment pdf will specify where to submit.
Project (CS686)
- Deadline at 2024-08-09 23:59 PM.
- The project is meant to be finished individually.
- The project proposal deadline in on July 4th.
- 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 1 page description about the problem you aim to solve, including background, motivation, proposed methods.
- The project needs to be related to the course, including search algorith, hidden markov model, reinforcement learning, neural networks.
- Some example projects are listed in this website
- 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 are not copied from public repository. Any violation will be seen as plagiarism with serious consequences.
Timetable
Lectures will take place twice per week as follows
- Section 002: Tuesday/Thursday (10:00AM-11:20Am MC 2035)
- Section 001: Tuesday/Thursday (11:30AM-12:50PM MC 2035)
- Section 003: Tuesday/Thursday (1:00PM-2:20PM MC 2035)
Exams:
- Final Exam: 2024-08-07 19:30 at STC 1012
Office Hours are as follows:
- Wenhu Chen: Thur 3:00pm-4:00pm, DC2635 (in May and July)
- Pascal Poupart: Tuesday and Wednesday 4-5pm, DC2514 (throughout June)
- TAs will host special office hours for each assignment
- Assignment 1 TA office hours: See Piazza post
- Assignment 2 TA office hours: See Piazza post
- Assignment 3 TA office hours: See Piazza post
Structure
The course content will be delivered in a lecture format, with four assignments, and a final exam.
TA responsibility
- Uncertainty: Yudong Luo
- Intro to ML: Aref Jafari and RuiMing Xiong
- Decision Process & Reinforcement Learning: Zeou Hu, Shuhui Zhu, and Varun Dhanraj
- Search Algorithm: Dake Zhang and Sabrina Mokhtari
- Deep Learning: Jheng-Hong Yang
Reading
Primary Texts:
David Poole and Alan Mackworth "Artificial Intelligence: Foundations of Computational Agents". Cambridge University Press, (1st edition: 2010, 2nd edition: 2017).
(available online. The section references below are to the 2nd edition.)
And the useful and informative resources with lots of code for the examples in the book
See online resources and in particular the
Python programs.
Secondary Readings:
Russell and Norvig
Artificial Intelligence
Ian Goodfellow and Yoshua Bengio and Aaron Courville
Deep Learning
Richard Sutton and Andrew Barto
Reinforcement Learning: An Introduction
Assessment
For CS486 students:
- 3 Assignments (30% - to be done individually - dates to be announced).
- 10 weekly after-class quiz (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%).
How and Where to submit
- Assignments are to be done individually unless otherwise stated.
- Submit assignments and receive marks through Learn.
- If you are not familiar with Learn, see the instructions for using dropboxes to hand in assignments.
- 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.
Other materials (videos, software, handouts, etc)
University of Waterloo Academic Integrity Policy
The University of Waterloo Senate Undergraduate Council has also approved the following message outlining University of Waterloo policy on academic integrity and associated policies.
Academic Integrity
In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. Check the Office of Academic Integrity's website for more information.
All members of the UW community are expected to hold to the highest standard of academic integrity in their studies, teaching, and research. This site explains why academic integrity is important and how students can avoid academic misconduct. It also identifies resources available on campus for students and faculty to help achieve academic integrity in, and our, of the classroom.
Grievance
A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70 - Student Petitions and Grievances, Section 4. When in doubt please be certain to contact the department's administrative assistant who will provide further assistance.
Discipline
A student is expected to know what constitutes academic integrity, to avoid committing academic offenses, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course professor, academic advisor, or the Undergraduate Associate Dean. For information on categories of offenses and types of penalties, students should refer to Policy 71-Student Discipline. For
typical penalties check Guidelines for the Assessment of Penalties.
Avoiding Academic Offenses
Most students are unaware of the line between acceptable and unacceptable academic behaviour, especially when discussing assignments with classmates and using the work of other students. For information on commonly misunderstood academic offenses and how to avoid them, students should refer to the Faculty of Mathematics Cheating and Student Academic Discipline Policy.
Appeals
A decision made or a penalty imposed under Policy 70, Student Petitions and Grievances (other than a petition) or Policy 71, Student Discipline may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 - Student Appeals.
Note for students with disabilities
The AccessAbility Services Office (AAS), located in Needles Hall, Room 1401, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the AAS at the beginning of each academic term.