Simon Fraser University
Engaging the World

Course Outlines

Fall 2025 - CMPT 726 G100

Machine Learning (3)

Class Number: 5536

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 3 – Dec 2, 2025: Wed, 3:30–4:20 p.m.
    Burnaby

    Sep 3 – Dec 2, 2025: Fri, 2:30–4:20 p.m.
    Burnaby

Description

CALENDAR DESCRIPTION:

Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.

COURSE DETAILS:

Machine Learning is the study of computer algorithms that improve automatically through experience. Machine learning algorithms play an important role in industrial applications and commercial data analysis. The goal of this course is to present students with both the theoretical justification for and practical application of, machine learning algorithms. Students in the course will gain hands-on experience with major machine learning tools and their applications to real-world data sets. This course will cover techniques in supervised and unsupervised learning, neural networks / deep learning, the graphical model formalism, and algorithms for combining models. This course is intended for graduate students who are interested in machine learning or who conduct research in fields that use machine learning, such as computer vision, natural language processing, data mining, bioinformatics, and robotics. No previous knowledge of pattern recognition or machine learning concepts is assumed, but students are expected to have or obtain, background knowledge in mathematics and statistics.

Topics

  • Graphical models: directed and undirected graphs
  • Inference algorithms: junction tree, belief propagation, variational inference, Markov Chain Monte Carlo, Gibbs sampling
  • Temporal models and algorithms: hidden Markov Models, Kalman filtering, particle filtering
  • Classification: nearest neighbour, support vector machines, decision trees, naive Bayes, Fisher's linear discriminant
  • Regression: linear regression, logistic regression, regularization
  • Unsupervised learning: spectral clustering, kmeans
  • Expectation-maximization
  • Deep learning

Grading

NOTES:

The course grade will be based on homework assignments, a project, and exam.

Materials

MATERIALS + SUPPLIES:

Reference Books

  • The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer-Verlag, 2009, 9780387848570
  • Machine Learning, Tom Mitchell, McGraw Hill, 1997, 9780070428072
  • Pattern Classification (2nd ed.), Richard O. Duda, Peter E. Hart, and David G. Stork, Wiley Interscience, 2000, 9780471056690
  • All of Statistics, Larry Wasserman, Springer, 2010, 9781441923226

REQUIRED READING:

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

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 Graduate 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).

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

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.