Fundamentals of Machine Learning

Fall 2024 (COMP451)

Administrative

Class time: Mondays and Wednesdays 11:30 - 1 pm
Class location: Birks 205
Instructor: Siamak Ravanbakhsh
Office hour: Wednesdays 1 - 2 pm (ENGMC 325)
TA team:
- (tbd.tbd)
- TBD (tbd.tbd)
TA Office hours: TBD



Course Description

The course will introduce core concepts of machine learning, with an emphasis on the computational, statistical and mathematical foundations of the field. We will study models for both supervised learning and unsupervised learning, introducing these models alongside foundational machine learning concepts, such as maximum likelihood estimation, regularization, information theory, and gradient-based optimization.


Prerequisites:

This course requires programming skills (Python and NumPy), calculus (e.g., MATH 222), linear algebra (e.g., MATH 223), probability (MATH 323) and algorithms (COMP 251).

Course Material:

Assignments, slides, and other course materials are posted on myCourses.
Jupyter Notebooks accompanying some topics are available on GitHub .
Lectures are not recorded.

Textbook:

There are no required textbook but we closely follow the presentation of the following book:
Probabilistic Machine Learning: An Introduction by Kevin Murphy (2022) ( McGill access , GitHub draft)

List of Topics Includes (subject to minor change)

Linear Classification and Regression
Maximum Likelihood
Gradient Descent Methods
Regularization
Neural Networks and Deep Learning
Exemplar Based Methods
Decision Trees, Bagging and Random Forests
Clustering
Dimensionality Reduction

Evaluation

Evaluation will be based on the following components (see myCourses for details):
- Theory and Programming Assignments (45%)
- Group Projects (50%)
- Class participation (5%)


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.