Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - MATH 475 D100

Mathematical Topics in Data Science (3)

Class Number: 5344

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Mon, Wed, Fri, 11:30 a.m.–12:20 p.m.
    Burnaby

  • Prerequisites:

    MATH 242, MATH 240 or MATH 232 and STAT 270, all with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

An exploration of the mathematics of data science. Analysis of the foundations of algorithms currently used in the field. Potential topics to be covered include: machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks and random graph models. Students may repeat this course for further credit under a different topic.

COURSE DETAILS:

This course provides a rigorous mathematical foundation for modern data science and machine learning. Students will develop both theoretical understanding and practical implementation skills, progressing from fundamental concepts like clustering and linear algebra through optimization, spectral methods, graph theory, probabilistic models, and neural networks. The course emphasizes mathematical proofs, algorithmic analysis, and efficient implementation.

Grading

  • Homework (5-6 assignments) 25%
  • Labs (6 programming assignments) 15%
  • Midterm Exam 15%
  • Presentation 10%
  • Class Participation 5%
  • Final Exam 30%

Materials

MATERIALS + SUPPLIES:

Lecture notes and selected readings will be provided in class. 

RECOMMENDED READING:

Mathematical methods in data science, S. Roch

Linear algebra, data science and machine learning, Calder & Olver

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

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.