CS486/686: Introduction to Artificial Intelligence
Dr. Wenhu Chen, (wenhuchen [at] uwaterloo [dot] ca)
- TAs (email should end with @uwaterloo.ca):
- Mokhtari, Sabrina (s4mokhta@)
- Haddady, Aryan (ahaddady@)
- Yu, Yonghan (y443yu@)
- Gano, Jess (jgano@)
- Thakur, Nandan (n3thakur@)
- 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.
- Assignments has both written part and coding part.
- The assignment pdf will specify where to submit.
- Graduates are required to finish a project.
- Undergraduates are not required to finish a project. The project will be added as bonus to the original score.
- Students can team up to finish the project, the maximum number teammate is 3.
- Make sure to describe clearly the labor division.
- 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.
Who should I ask for help?
The TAs are distributed to handle queries/assignments/coding/office hours regarding different parts of the course.
- If you have questions regarding "Search Algorithm", please consult Sabrina or Aryan.
- If you have questions regarding "Uncertainty Estimation", please consult Yonghan.
- If you have questions regarding "Markov Decision Process", please consult Jess.
- If you have questions regarding "Machine Learning", please consult Nandan.
Lectures will take place twice per week as follows
- Section 001: Tuesday/Thursday (2:30-3:50PM MC 4061)
- Section 002: Tuesday/Thursday (4:00-5:20PM MC 4061)
- Final Exam: April 18th Tuesday, 7:30 PM - 10:00 PM (https://uwaterloo.ca/registrar/final-examinations/exam-schedule)
Office Hours are as follows:
- Wenhu Chen: Friday 12:00pm-1:00pm (ZOOM link posted on Piazza)
- TAs will host special office hours for each assignment
- Assignment 1 TA office hours (Sabrina and Aryan): Jan 27th, 10:30 AM - 12:00 PM and Jan 30th, 10:30 AM - 12:00 PM
- Assignment 2 TA office hours (Yonghan): Feb 17 from 11:30 AM and Feb 27 from 11:30 AM to 1 PM
- Assignment 3 TA office hours (Jess): TBD
- Assignment 4 TA office hours (Nandan): TBD
The course will consist of two 1.5-hour in-class sessions per week.
The course content will be delivered in a lecture format, with four assignments, and a final exam.
Graduate students must complete a project (optional for undergraduates).
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
Russell and Norvig Artificial Intelligence
Ian Goodfellow and Yoshua Bengio and Aaron Courville Deep Learning
For CS486 students:
- 4 Assignments (50% - to be done individually - dates to be announced).
- Two and a half hour written final examination (50% and must pass the final to pass the course).
- Optional project (10% bonus).
For CS686 (grad) students:
- 4 Assignments (20% - to be done individually - dates to be announced).
- Two and a half hour written final examination (40%).
- Project (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.
- Submit final projects on LEARN before the final exam.
Overview: Search Algorithm
Overview: Uncertainty Estimation
Sun, Feb 5
A1 due, A2 posted
Overview: Markov Decision Process
Thu, Feb 16
Project Proposal Due
Overview: Machine Learning and Deep Learning
Tue, Mar 28
L21: Artificial Neural Networks Part 3
Thu, Mar 30
L22: Biomedical AI (Guest Lecture by Yang Lu)
Fri, Apr 21
Project Final Report due
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.
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.
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.
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.
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.