Fall 2021 - CMPT 726 G100

Machine Learning (3)

Class Number: 4661

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 8 – Dec 7, 2021: Mon, 4:30–6:20 p.m.
    Burnaby

    Sep 8 – Dec 7, 2021: Wed, 5:20–6:20 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

  • 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

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 FALL 2021

Teaching at SFU in fall 2021 will involve primarily in-person instruction, with approximately 70 to 80 per cent of classes in person/on campus, with safety plans in place.  Whether your course will be in-person or through remote methods will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware 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.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.