Computing Science Course Outlines

Course Outline - CMPT 726 - Machine Learning



Catalog Number









2021 Spring (1211)

Machine Learning

Ke Li   

Burnaby Mountain Campus

Calendar Objective/Description

Machine Learning

Instructor's Objectives

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.




  • (Generalized) linear models: linear regression, ridge regression, logistic regression
  • Non-linear models: kernel ridge regression, SVMs, neural networks, k-nearest neighbours
  • Regression, binary classification, multinomial classification
  • Optimization: gradient descent, stochastic gradient descent
  • Unsupervised learning: principal components analysis, auto-encoders, clustering


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

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

Academic Honesty Statement

Academic honesty plays a key role in our efforts to maintain a high standard of academic excellence and integrity. Students are advised that ALL acts of intellectual dishonesty will be handled in accordance with the SFU Academic Honesty and Student Conduct Policies ( ).

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