Simon Fraser University
Engaging the World

Course Outlines

Spring 2025 - CMPT 420 D100

Deep Learning (3)

Class Number: 5460

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 6 – Apr 9, 2025: Mon, 12:30–2:20 p.m.
    Burnaby

    Jan 6 – Apr 9, 2025: Wed, 12:30–1:20 p.m.
    Burnaby

  • Prerequisites:

    CMPT 410 or CMPT 419 (Machine Learning), with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

In machine learning, many recent successes have been achieved using neural networks with several layers, so-called deep neural networks. Convolutional neural nets, autoencoders, recurrent neural nets, long-short term memory networks, and generative adversarial networks will be presented. Students will look at techniques for training them from data, and applications. Students with credit for CMPT 728 may not take this course for further credit.

COURSE DETAILS:

Machine learning has become the main framework for building programs that perform intelligent tasks. In fields such as computer vision and natural language processing, many recent successes have been achieved using neural nets with several layers, known as deep neural nets. This course is an introduction to deep neural nets, techniques for training them from data, and significant applications. The main learning outcomes are 1) sufficient practical experience with deep learning to apply current techniques to real-life problems 2) sufficient theoretical understanding of deep neural nets to analyze and improve their performance.

Course Requirement Notes: You will find it difficult to succeed without a background in machine learning and the requisite mathematics (calculus, linear algebra, probability). There will be a background quiz to give you feedback on whether you are prepared.

Grading

NOTES:

  • homework, 30%
  • quizzes, 15%
  • final exam, 35%
  • final project 20%
Grading will be based on homework (3-5), quizzes (3-5), final exam, and a final project. The main component of the assignments will be applying neural networks to datasets. Students must attain at least 50% on the final exam to obtain a clear pass (C- or better).

Materials

MATERIALS + SUPPLIES:

Reference Books

  • Deep Learning, Goodfellow, Bengio, and Courville, MIT Press, 2016, 9780262035613, Available on-line at http://www.deeplearningbook.org
  • Introduction to Deep Learning, Eugene Charniak, MIT Press, 2018, 9780262039512

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.

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.