Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - CMPT 410 D100

Machine Learning (3)

Class Number: 5467

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Mon, 12:30–1:20 p.m.
    Burnaby

    Jan 5 – Apr 10, 2026: Thu, 12:30–2:20 p.m.
    Burnaby

  • Exam Times + Location:

    Apr 17, 2026
    Fri, 3:30–6:30 p.m.
    Burnaby

  • Prerequisites:

    CMPT 310 and MACM 316, both with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

Machine Learning (ML) is the study of computer algorithms that improve automatically through experience. This course introduces students to the theory and practice of machine learning, and covers mathematical foundations, models such as (generalized) linear models, kernel methods and neural networks, loss functions for classification and regression, and optimization methods. Students with credit for CMPT 419 under the title "Machine Learning" may not take this course for further credit.

COURSE DETAILS:

Machine learning is the study of computer algorithms that improve automatically through experience, which play an increasingly important role in artificial intelligence, computer science and beyond. The goal of this course is to introduce students to machine learning, starting from the foundations and gradually building up to modern techniques. Students in the course will learn about the theoretical underpinnings, modern applications and software tools for applying deep learning. This course is intended to be an introductory course for students interested in conducting research in machine learning or applying machine learning, and should prepare students for more advanced courses, such as CMPT 727 and CMPT 728. No previous knowledge of machine learning is assumed, but students are expected to have solid background in calculus, linear algebra, probability and programming using Python.

Topics

  • Mathematical foundations: review of linear algebra, multivariate calculus and probability
  • (Generalized) linear models: linear regression, ridge regression, logistic regression
  • Non-linear models: support vector machines, neural networks, k-nearest neighbours
  • Regression, binary classification, multinomial classification
  • Optimization: gradient descent, stochastic gradient descent, Lagrangian duality

Grading

NOTES:

The course grade will be based on quizzes, assignments, exam and participation.

Students must attain a passing grade on the final exam in order to receive a passing grade in the course.

Materials

MATERIALS + SUPPLIES:

Reference Books:

Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, MIT Press, 2012, 9780262018029

The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer-Verlag, 2009, 9780387848570

All of Statistics, Larry Wasserman, Springer, 2010, 9781441923226

Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006, 9780387310732

Machine Learning, Tom Mitchell, McGraw Hill, 1997, 9780070428072

RECOMMENDED READING:

Mathematics for Machine Learning by G. Thomas: http://gwthomas.github.io/docs/math4ml.pdf

Mathematics for Machine Learning by M. P. Deisenroth, A. A. Faisal, C. S. Ong: https://mml-book.github.io/book/mml-book.pdf

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Undergraduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.