Applied Machine Learning

Winter 2026 (COMP551)

Administrative

Class: Mondays and Wednesdays 20:30 pm-4 pm, S1/4, Stewart Biology Building
Co-Instructors: Pascale Gourdeau and Siamak Ravanbakhsh
Office hours: TBD
TA team: TBD

Course Description

This course covers a selected set of topics in machine learning. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear regression and classification, gradient-based optimization, deep neural networks, as well as unsupervised learning techniques such as dimensionality reduction and clustering.


Prerequisites:

This course requires programming skills (Python and NumPy) and knowledge of probability (e.g., MATH 323 or ECSE 305), calculus (e.g., MATH 222), linear algebra (e.g., MATH 223), and algorithms (e.g., COMP 251). For more information on official requirements see the course prerequisites and restrictions here.

Course Material:

Assignments, slides, tutorials, code links, project descriptions and other course materials are posted on myCourses.

Textbooks:

There are no required textbook but the topics are covered by the following books:
[Murphy] Probabilistic Machine Learning: An Introduction by Kevin Murphy (2022)
[Bishop] Pattern Recognition and Machine Learning by Christopher Bishop (2007)
[Goodfellow]    Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016)
[Murphy] Machine Learning: A Probabilistic Perspective by Kevin Murphy (2012)

Other Related References
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009)
Information Theory, Inference, and Learning Algorithms, by David MacKay (2003)
Bayesian Reasoning and Machine Learning , by David Barber (2012).
Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David (2014)
Foundations of Machine Learning, by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (2018)
Dive into Deep Learning , by Aston Zhang, Zachary Lipton, Mu Li, and Alexander J. Smola (2019)
Mathematics for Machine Learning , by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong (2019)
A Course in Machine Learning, by Hal Daumé III (2017)
Hands-on Machine Learning with Scikit-Learn and TensorFlow, by Aurélien Géron (2017)

Tentative Outline

  • Syllabus and Introduction
  • Maximum Likelihood and Bayesian Inference
Linear Classification and Regression
  • Naive Bayes
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
Training and Evaluation
  • Gradient Descent
  • Regularization
  • Generalization
Deep Networks
  • Multilayer Perceptron
  • Automatic Differentiation
  • Deep Learning with Image Data
  • Deep Learning with Sequential Data
Nonparametric Methods
  • Nearest Neighbours
  • Decision Trees and Random Forests
Unsupervised Learning
  • Clustering
  • Dimensionality Reduction

Evaluation

Evaluation will be based on the following components:
Regular quizzes (15%) online in myCourses
Group mini-projects (50%) group assignments
First exam (15%)
Second exam (25%)




Assessments in this course are governed by the Policy on Assessment of Student Learning (PASL), which provides a set of common principles to guide the assessment of students’ learning. Also see Faculty of Science-specific rules on the implementation of PASL.

Accommodations

Legally mandated academic accommodations are handled by Student Accessibility and Achievement. For more information see https://www.mcgill.ca/access-achieve/

Academic considerations for assessments that are missed or late for valid reasons will be provided at the instructor's discretion. As per the new Quebec guidelines, medical notes are not required for absences of less than 5 days. Note that repeated, similar requests for academic considerations in this course are unlikely to be granted.

Language of Submission

In accord with McGill University’s Charter of Students’ Rights, students in this course have the right to submit in English or in French written work that is to be graded. This does not apply to courses in which acquiring proficiency in a language is one of the objectives.” (Approved by Senate on 21 January 2009) Conformément à la Charte des droits de l’étudiant de l’Université McGill, chaque étudiant a le droit de soumettre en français ou en anglais tout travail écrit devant être noté, sauf dans le cas des cours dont l’un des objets est la maîtrise d’une langue. (Énoncé approuvé par le Sénat le 21 janvier 2009)

Academic Integrity

McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures (Approved by Senate on 29 January 2003) (See McGill’s guide to academic honesty for more information).

In the event of extraordinary circumstances beyond the University’s control, the content and/or assessment tasks in this course are subject to change and students will be advised of the change.