Fall 2019 - STAT 652 G100

Statistical Learning and Prediction (3)

Class Number: 4618

Delivery Method: In Person


  • Course Times + Location:

    Tu 10:30 AM – 11:20 AM
    AQ 3181, Burnaby

    Th 9:30 AM – 11:20 AM
    AQ 3181, Burnaby

  • Exam Times + Location:

    Dec 6, 2019
    12:00 PM – 3:00 PM
    RCB IMAGTH, Burnaby

  • Prerequisites:

    STAT 302 or STAT 305 or STAT 350 or BUEC 333 or equivalent.



An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Open only to graduate students in departments other than Statistics and ActSci. Students with credit for STAT 452 may not take this course for further credit.



1. Statistical Learning and Prediction
2. Measuring prediction error
3. Linear regression essentials and extensions
4. Classification: Predicting categorical data
5. Variable selection in linear regression
6. Non-linear regression methods
7. Trees and ensembles
8. Additional modern prediction methods
9. Unsupervised learning: clustering and dimension reduction


  • Assignments 10%
  • Quizzes 10%
  • Midterm 30%
  • Final Project 50%


Above grading is subject to change.



An Introduction to Statistical Learning with Applications in R. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013). New York: Springer. 

Book is available for free on-line through the SFU Libarary
ISBN-13: 978-1461471370.

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:

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