Fall 2020 - STAT 452 D200

Statistical Learning and Prediction (3)

Class Number: 8707

Delivery Method: In Person

Overview

  • Course Times + Location:

    TBA
    REMOTE LEARNING, Burnaby

    TBA
    REMOTE LEARNING, Burnaby

  • Exam Times + Location:

    Dec 13, 2020
    7:00 PM – 10:00 PM
    REMOTE LEARNING, Burnaby

  • Prerequisites:

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

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.

COURSE DETAILS:

Outline:

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

Mode of teaching:

Lecture: asynchronous (recorded)
Tutorial: asynchronous (recorded)
Office hours: synchronous, time TBD
Final exam: synchronous; date: TBA. Invigilation will be done using Zoom, required.

This course is accredited under the Canadian Institute of Actuaries (CIA) University Accreditation Program (UAP). Achievement of the minimum required grades in accredited courses may provide credit for preliminary exams. Please note that a combination of courses may be required to achieve exam credit. 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) and the CIA FAQ (www.cia-ica.ca/docs/default-source/miscellaneous/uap/2018-uap-faq-and-career-brochure.pdf).

Grading

  • Assignments 10%
  • Individual Project 1 25%
  • Individual Project 2 25%
  • Final exam 40%

NOTES:

Above grading is subject to change.

Materials

MATERIALS + SUPPLIES:

Access to high-speed internet, webcam

Additional required software: Access to R, either installed or online (e.g., through Jupyter notebooks)

REQUIRED READING:

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.

Department Undergraduate Notes:

Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or csdo@sfu.ca


Tutor Requests:
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/need-a-tutor-.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 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 2020

Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges 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. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).