Fall 2022 - STAT 652 G100
Statistical Learning and Prediction (3)
Class Number: 4672
Delivery Method: In Person
Course Times + Location:
We 9:30 AM – 10:20 AM
AQ 3182, Burnaby
Fr 8:30 AM – 10:20 AM
AQ 3182, Burnaby
Exam Times + Location:
Dec 17, 2022
3:30 PM – 6:30 PM
SSCB 9200, Burnaby
Prerequisites:STAT 302 or STAT 305 or STAT 350 or STAT 604 or STAT 605 or ECON 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
- Assignment 10%
- Individual Project 1 10%
- Individual Final Project 15%
- Midterm Test 30%
- Final 35%
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 online through the SFU Library
REQUIRED READING NOTES:
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Graduate Studies Notes:
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