Fall 2022 - STAT 452 D100
Statistical Learning and Prediction (3)
Class Number: 4669
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
1 778 782-6998
Prerequisites:STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-.
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. Quantitative.
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
This course is accredited under the Canadian Institute of Actuaries (CIA) University Accreditation Program (UAP). Details of required courses and grades at Simon Fraser University are available here (https://www.cia-ica.ca/membership/university-accreditation-program-home/accredited-universities/accredited-university-detail?pav_universityid=236ca8c4-60e5-e511-80b9-00155d111030).
In addition to the specific university’s internal policies on conduct, including academic misconduct, candidates pursuing credits for writing professional examinations shall also be subject to the Code of Conduct and Ethics for Candidates in the CIA Education System and the associated Policy on Conduct and Ethics for Candidates in the CIA Education System. For more information, please visit Obtaining UAP Credits (https://www.cia-ica.ca/membership/university-accreditation-program-home/information-for-candidates/obtaining-uap-credits).
- 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 Libarary
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Department Undergraduate Notes:
Students with Disabilities:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or firstname.lastname@example.org.
Students looking for a tutor should visit https://www.sfu.ca/stat-actsci/all-students/other-resources/tutoring.html. We accept no responsibility for the consequences of any actions taken related to tutors.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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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