Fall 2024 - STAT 452 D100
Statistical Learning and Prediction (3)
Class Number: 3031
Delivery Method: In Person
Overview
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Course Times + Location:
Sep 4 – Dec 3, 2024: Tue, 1:30–2:20 p.m.
Location: TBASep 4 – Dec 3, 2024: Thu, 12:30–2:20 p.m.
Location: TBA
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Instructor:
Owen Ward
1 778 782-7782
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Prerequisites:
STAT 260 and one of STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-.
Description
CALENDAR DESCRIPTION:
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.
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 caladmin@sfu.ca.
Tutor Requests:
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.
Registrar Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website 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