Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - CMPT 420 D100

Deep Learning (3)

Class Number: 5476

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Mon, 2:30–4:20 p.m.
    Burnaby

    Jan 5 – Apr 10, 2026: Wed, 2:30–3:20 p.m.
    Burnaby

  • Prerequisites:

    CMPT 310, CMPT 410 or CMPT 419 (Machine Learning), both 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 and deep neural networks are key approaches for building intelligent systems. This course will explore both the foundations and applications of deep learning. The first part will cover fundamental topics related to deep neural networks, including complexity, optimization, and generalization. The second part will focus on classical deep networks for various applications, such as computer vision, sequence data, and natural language processing. The final part will delve into more advanced topics currently being explored in research.

COURSE-LEVEL EDUCATIONAL GOALS:

The course will be at an advanced undergraduate/beginner graduate level. The course will presume a functional understanding of probability, statistics, linear algebra. The assignments and project will have substantial programming components.

Grading

NOTES:

Grading

  • Class Participation and Discussion: 5%
  • Assignments: 35% (four homework).
    • There will be some problems optional to undergraduate students. Undergraduate students can win bonus credits from these optional problems to compensate for any penalty received from required problems (i.e. the final score will not exceed the total).
  • Final Exam (in class): 35%
  • Projects (To be done in groups of 2~3): 25%
    • Project Proposal: 5% (2 pages with abstract, introduction & motivation, plans)
    • Final Project Report: 10% (5 pages with abstract, introduction & motivation, related work, methods, results, conclusion)
    • Template for Proposal / Report: LaTeX, MS Word
    • Project Presentation: 10%

REQUIREMENTS:

Late Submissions: All assignments are due on the respective due date at 11:59 pm Pacific Time. Only on-time assignments will be accepted.
Academic Integrity: You are permitted to utilize external resources for assignments, provided that proper acknowledgment is given in the appropriate location. For more details about the honor code, see SFU Student Academic Integrity Policy.

Materials

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:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


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.