Courses
Introduction to Machine Learning

Introduction to Machine Learning

Course Information
Course syllabus

Lectures (Tentative)
Date Topic Slides Notes
00 Sep 09, 2026 Perceptron pdf pdf
01 Sep 14, 2026 Linear Regression pdf pdf
02 Sep 16, 2026 Logistic Regression pdf pdf
03 Sep 21, 2026 Gradient Optimization
04 Sep 23, 2026 Fully Connected NNs pdf pdf
05 Sep 28, 2026 Convolutional NNs pdf
06 Sep 30, 2026 Recurrent NNs pdf pdf
07 Oct 05, 2026 Reproducing Kernels pdf pdf
08 Oct 07, 2026 Ensemble pdf pdf
Oct 12, 2026 reading week
Oct 14, 2026 reading week
09 Oct 19, 2026 Gaussian Mixtures pdf pdf
10 Oct 21, 2026 GANs pdf pdf
11 Oct 26, 2026 Flows pdf
12 Oct 28, 2026 Auto-Encoder
13 Nov 02, 2026 Attention pdf pdf
14 Nov 04, 2026 LLMs
15 Nov 09, 2026 Optimal Transport pdf
16 Nov 11, 2026 Diffusion pdf
17 Nov 16, 2026 RLHF pdf
18 Nov 18, 2026 JEPA pdf
19 Nov 23, 2026 Robustness pdf
20 Nov 25, 2026 Fairness pdf
21 Nov 30, 2026 Privacy pdf
22 Dec 02, 2026 Valuation pdf
23 Dec 07, 2026 Causality pdf
!!! Dec xx, 2026 Final exam x:xx - x:xx

Assignment (Tentative)

Assignment 1: Due at Sep 30, 2026
Assignment 2: Due at Oct 26, 2026
Assignment 3: Due at Nov 16, 2026
Assignment 4: Due at Dec 07, 2026

Project

Project

Per the instructor’s approval, you may substitute the final exam with a research report. Possible choices include:

  • an attempt to beat the state-of-the-art performance on an interesting dataset
  • an unexpected application of ML to a different field
  • a novel algorithm to address a need in machine learning
  • a (new or improved) theoretical analysis of an ML algorithm (new or old)
  • a submission to the ML reproducibility challenge

Project template: download zip; main file: project.tex; references: references.bib

Project proposal: You need to submit a project proposal (10%) by Nov 9, 2026. Please write your proposal using the provided template and limit yourself to 2 pages + 1 page solely for figures and tables (if needed) + 1 page solely for reference. Please elaborate the following:

  • What is the project about? What is the goal?
  • Initial literature search: what has been done? What remains open?
  • Feasibility study: convince us your proposal is within reach. If possible, include simple toy examples or quick experiments.
  • Dataset, evaluation and computing (if applicable): make sure you have all the resource for your project. Talk to us if you need help.

Project report: You need to submit a project report (30%) by Dec 15, 2026 that summarizes all your findings (empirical, algorithmic, theoretical). We expect there is an introduction section, a background section, a main result section, and a conclusion section. Depending on your project, you may include an experimental section and/or discussion section. Please always give proper citations to prior work or results. Be precise and concise. We expect the report to be at most 8 pages (excluding references). Your project report will be evaluated by its clarity, significance, rigor, presentation, and completeness.

Textbook

There is no required textbook, but the following fine texts are recommended.

Optional
For those who need to refresh math (all of us?)

Resource

Use of Artificial Intelligence Tools

The use of generative AI tools (such as ChatGPT, Claude, Gemini, GitHub Copilot, and similar tools) is permitted in this course as a learning aid. However, all submitted work must reflect your own understanding and intellectual effort. You are responsible for completing the assigned work independently and for being able to explain and justify everything you submit.

AI tools may be used, for example, to clarify concepts, obtain feedback on your work, improve writing, or assist with debugging. They should not be used to generate solutions or substantial portions of an assignment that you then submit as your own work.

Whenever you use an AI tool in completing an assignment, you must include a brief declaration stating which tool(s) you used and how you used them. If you did not use any AI tools, no declaration is necessary.

You remain fully responsible for the correctness, originality, and integrity of all work you submit. Undeclared or inappropriate use of AI may be treated as a violation of the course’s academic integrity requirements.

Policy

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 university website for more information.

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 an academic offence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties check Guidelines for the Assessment of Penalties.

Appeals: A decision made or 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 Office for Persons with Disabilities (OPD), located in Needles Hall, Room 1132, 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 OPD at the beginning of each academic term.

Mental Health: If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support.

On-campus Resources

Off-campus Resources

  • Good2Talk (24/7): Free confidential help line for post-secondary students. Phone: 1-866-925-5454
  • Here 24/7: Mental Health and Crisis Service Team. Phone: 1-844-437-3247
  • OK2BME: set of support services for lesbian, gay, bisexual, transgender or questioning teens in Waterloo. Phone: 519-884-0000 extension 213

Diversity: It is our intent that students from all diverse backgrounds and perspectives be well served by this course, and that students’ learning needs be addressed both in and out of class. We recognize the immense value of the diversity in identities, perspectives, and contributions that students bring, and the benefit it has on our educational environment. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups. In particular:

  • We will gladly honour your request to address you by an alternate/preferred name or gender pronoun. Please advise us of this preference early in the semester so we may make appropriate changes to our records.
  • We will honour your religious holidays and celebrations. Please inform of us these at the start of the course.
  • We will follow AccessAbility Services guidelines and protocols on how to best support students with different learning needs.