Spring 2021 - CMPT 726 G100

Machine Learning (3)

Class Number: 8496

Delivery Method: In Person

Overview

  • Course Times + Location:

    We 11:30 AM – 12:20 PM
    REMOTE LEARNING, Burnaby

    Fr 10:30 AM – 12:20 PM
    REMOTE LEARNING, Burnaby

  • Exam Times + Location:

    Apr 26, 2021
    3:30 PM – 6:30 PM
    REMOTE LEARNING, 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:

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.

COURSE-LEVEL EDUCATIONAL GOALS:

Topics

  • (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

Grading

NOTES:

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

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

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

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN SPRING 2021

Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).