Spring 2024 - MATH 775 G100
Mathematical Data Science (3)
Class Number: 5491
Delivery Method: In Person
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 with credit for MATH 475 may not take this course for further credit. APMA 940 will be accepted in lieu of MATH 775.
- Homework 36%
- Presentation 20%
- Participation 10%
- Final Project 34%
MATERIALS + SUPPLIES:
Lecture notes and selected readings will be provided in class.
Foundations of Machine Learning, 2nd ed.
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
MIT Press, 2018
Foundations of Data Science
Avrim Blum, John Hopcroft, Ravindran Kannan
Cambridge University Press, 2020
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.
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
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