Simon Fraser University
Engaging the World

Course Outlines

Fall 2024 - STAT 440 D100

Learning from Big Data (3)

Class Number: 3030

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 4 – Dec 3, 2024: Mon, 2:30–4:20 p.m.
    Burnaby

    Oct 15, 2024: Tue, 2:30–4:20 p.m.
    Burnaby

    Sep 4 – Dec 3, 2024: Wed, 2:30–3:20 p.m.
    Burnaby

  • Prerequisites:

    90 units including STAT 350 with a minimum grade of C- and one of STAT 341, STAT 260, or CMPT 225, with a minimum grade of C-, or instructor approval. STAT 240 is also recommended.

Description

CALENDAR DESCRIPTION:

A data-first discovery of advanced statistical methods. Focus will be on a series of forecasting and prediction competitions, each based on a large real-world dataset. Additionally, practical tools for statistical modeling in real-world environments will be explored.

COURSE DETAILS:

STAT 440 is suitable for senior students who have a minimum of 90 units.

Course Outline

The course will be split into several modules. Each module will focus on a particular dataset. At the start of each module, students will be randomly divided into teams. A subset of the dataset will be given to all teams (the training data) and the rest of the dataset will be withheld (the test data). Students will learn modern machine learning methods for predicting aspects of the test data based on the training data. This test/train paradigm is often encountered in both academic and industrial settings. The methods will include bagging, boosting, deep learning, model blending and cross-validation. The students will learn how to implement these methods using standard software packages such as scikit-learn and tensorflow.They will use these methods (and any other techniques they wish) to predict aspects of the test data. During each module, teams will submit their predictions and see the results of those predictions on the withheld test data. Marks for competition results will be awarded based on the accuracy of their predictions.

Grading

  • Assignments 10%
  • Reading 20%
  • Projects 70%

NOTES:


Assignments and Grading Procedures

  • Three assignments will be given worth 10% each with problem sets following the methods taught in the lectures.
  • Five articles or papers will be assigned for reading, with in-class reading responses worth 5% each (best four out of five).
  • Students will work in teams of three or four on two projects (35% each). A dataset will be provided for each project. Marks for predictions of held-out target variables will be awarded based on performance relative to classmates and objective baselines. The project will also involve a written report covering insights and methods.
Above Grading is subject to change.

Materials

RECOMMENDED READING:

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition (A. Géron, 2023, O'Reilly)
  • Linear Algebra, 5th Edition (S. Friedberg, A. Insel, L. Spence, 2022, Pearson)
  • Probabilistic Machine Learning: An Introduction (K. Murphy, 2022, MIT Press)
  • The Kaggle Book (K. Banachewicz, L. Massaron, 2022, Packt Publishing) 
  • AWS Cookbook (J. Culkin, M. Zazon, 2023, O'Reilly)
  • Learning R (R. Cotton, 2013, O'Reilly)

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 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

RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.