CS 485/685 (Fall 2021)

Shai Ben-David

David R. Cheriton School of Computer Science

Course Outline

We will aim to folow the first two parts of the textbook (see below). Following the structure of the book, the first part of the course will be devoted to the general theory of machine learning, and in the second part we will go over some basic algorithms that are common in ML and explain the theory underlying them. The first 20 chapters of the book are all important for understanding machine learning. I hope to cover all of them, but I cannot guarantee it.


1) I prefer not to commit in advance to a lecture by lecture plan and leave it flexible. I believe that the more interactive the lecture are the more students learn from them, even if we end up covering less topics (after all, one can always just read the book). 

2) Overlap with CS480: I do not think there is going to be much of that. This course focuses on theory not implementation. I aim to explain the principles behind common ML algorithms rather than teaching how to use them in practice. This is a rather different focus than that of CS480 (even when both courses discuss the same algorithms).

3) There will be no coding and no implementations of algorithms in this course.


Lectures take place via Zoom on Mondays and Wednesdays 11:30am-12:50pm (Waterloo times).

The Zoom links are posted on Piazza.


Details on how to access the tutorials will be shared with you on Piazza and LEARN.

Course Team

name Office hours
Shai Ben-David
Tosca Lechner
Niki Hasrati
Tuesdays 1:30 p.m. – 2:30 p.m.

Grading Scheme

Assignments Average of the 4 best of 5 assignments - 40%
Mid-Term Assessment Counts only if better mark than the final - 20%
Final Assessment 40% or 60% if better than the midterm



All course material will be posted on the LEARN site.

The course textbook:

Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David. Students may access an electronic version of the textbook at: Free PDF copy

A full set of lecture videos :

A complete set of videos of my lectures of this course in 2015 is available at YouTube lecture videos

The material this term will overlap a lot of that.

Recording of the online lecture of this term :

Will be posted on LEARN after each lecture.

Schedule of assignements and exams

Assignment 1 posted: September 18, Submission deadline: October 1

Assignment 2 posted: October 2, Submission deadline: October 22

Assignment 3 posted: October 23 Submission deadline: November 5

Assignment 4 posted: November 6, Submission deadline: November 19

Assignment 5 posted: November 23, Submission deadline: December 7

The midterm will be posted on October 18 and due on October 19

Note for students with disabilities

UW's 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.

Academic Integrity and Students with Disabilities

Academic Integrity and Students with Disabilities

Academic Policies

This course adheres to the UW Senate's statement of academic integrity, specifically:

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. All members of the UW community are expected to hold to the highest standard of academic integrity in their studies, teaching, and research.

The Office of Academic Integrity's website contains detailed information on UW policy for students and faculty. 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 out 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.


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. When misconduct has been found to have occurred, disciplinary penalties will be imposed under Policy 71—Student Discipline. For information on categories of offenses and types of penalties, students should refer to Policy 71—Student Discipline.

Avoiding Academic Offenses

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 Guidelines.


A student may appeal the finding and/or penalty in a decision made under Policy 70—Student Petitions and Grievances (other than regarding a petition) or Policy 71—Student Discipline if grounds for an appeal can be established. Read Policy 72—Student Appeals.