CS486/686: Introduction to Artificial Intelligence

Winter 2023


People



Communication


Deliverables


Project

Who should I ask for help?

The TAs are distributed to handle queries/assignments/coding/office hours regarding different parts of the course.

Timetable

Lectures will take place twice per week as follows

Exams:

Office Hours are as follows:

Structure

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).


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

Assessment

For CS486 students:

For CS686 (grad) students:

How and Where to submit


Course Slides

Date
Lecture
Assessments

Overview: Search Algorithm

Tue, Jan 10
L1: Intro to Artificial Intelligence
Slides   Notes (Alice Gao) Video (Alice Gao)

Thu, Jan 12
L2: Uninformed Search
Slides   Notes (Alice Gao) Video (Alice Gao)

Tue, Jan 17
L3: Heuristic Search
Slides   Notes (Alice Gao) Video (Alice Gao)

Thu, Jan 19
L4: Constraint Satisfaction Problems
Slides   Notes (Alice Gao) Video (Alice Gao)
A1 posted

Tue, Jan 24

Overview: Uncertainty Estimation

Thu, Jan 26
L6: Introduction to Uncertainty and Probability
Slides   Notes (Alice Gao) Video (Alice Gao)

Tue, Jan 31
L7: Independence and Bayesian Networks
Slides   Notes (Alice Gao) Video (Alice Gao)

Thu, Feb 2
L8: Bayesian Networks
Slides   Notes (Alice Gao) Video (Alice Gao)

Sun, Feb 5

A1 due, A2 posted

Tue, Feb 7
L9: Variable Elimination Algorithm
Notes (Alice Gao) Video (Alice Gao)

Thu, Feb 9
L10: Hidden Markov Models Part 1
Notes (Alice Gao) Video (Alice Gao)

Tue, Feb 14
L11: Hidden Markov Models Part 2
Notes (Alice Gao) Video (Alice Gao)

Overview: Markov Decision Process

Thu, Feb 16
L12: Decision Theory and Decision Networks
Notes (Alice Gao) Video (Alice Gao)
Project Proposal Due

Reading Week
Tue, Feb 28
L13: Decision Networks
Notes (Alice Gao)

Thu, Mar 2
L14: Markov Decision Processes Part 1
Notes (Alice Gao) Video (Alice Gao)
A2 due, A3 posted

Tue, Mar 7
L15: Markov Decision Processes Part 2
Notes (Alice Gao) Video (Alice Gao)

Thu, Mar 9
L16: Reinforcement Learning Part 1
Notes (Alice Gao) Video (Alice Gao)

Tue, Mar 14
L17: Reinforcement Learning Part 2
Notes (Alice Gao) Video (Alice Gao)

Overview: Machine Learning and Deep Learning

Thu, Mar 16
L18: Intro to Machine Learning
Notes (Alice Gao) Video (Alice Gao)

Tue, Mar 21

Thu, Mar 23
L20: Artificial Neural Networks Part 1
Notes (Alice Gao) Video (Alice Gao)
A3 due,A4 posted

Tue, Mar 28
L21: Artificial Neural Networks Part 2
Notes (Alice Gao) Video (Alie Gao)


Overview: AI Application

Thu, Mar 30
L22: Biomedical AI (Guest Lecture by Yang Lu)

Tue, Apr 4
L23: Recap

Thu, Apr 6

A4 due

Tue, Apr 18

Final Exam

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.

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.