MATH 2740 (Math of Data Science)
This is the material for when I (Julien Arino) am teaching the course.
View the Project on GitHub julien-arino/math-of-data-science
MATH 2740: Mathematics of Data Science
This GitHub repo contains material related to the University of Manitoba course MATH 2740, Mathematics of Data Science and reflects the organisation of the course when I (Julien Arino) teach it.
Here, you will find the publicly available information: code, slides and some general information. All other information (syllabus, assignments, marks) is available through UM Learn.
Fall 2026 format
The course content is evolving this year, with graph theory becoming much less important in the second part of the course to avoid having to introduce another entire theoretical topic.
The course evaluation consists of Lab Tests (held during tutorials), a two-hour Midterm Examination and a Final Examination.
Office Hours: Tuesdays 14:00-16:00 in 316 St Paul’s. I can also see you from 16:00-18:00 on Tuesdays in the same location but please get in touch by email or in person during class to ensure I am there for that second block. Because of the ongoing renovation of Machray Hall, office hours are strictly limited to the above.
Exercises: The standalone booklet of exercises has answers, but these are posted after the corresponding tutorial.
This GitHub repo
On the GitHub version of the page, you have access to all the files. You can also download the entire repository by clicking the buttons on the left. (You can also of course clone this repo, but you will need to do that from the GitHub version of the site.)
Feel free to use the material in these slides or in the folders. If you find this useful, I will be happy to know.
Slides
- U01-introduction
- U02-math-preliminaries
- U03-least-squares
- U04-QR-SVD
- U05-PCA
- U06-linear-classification
- U07-distance-gram-MDS
- U08-nonnegative-matrices-digraphs
- U09-absorbing-chains-pagerank
The file with all the slides
Lecture notes
Lecture notes are posted on UM Learn.
Videos
The videos for the course are available on YouTube as a playlist. Note that the videos are far from perfect.. the slides and the videos will be reworked prior to the next instance of the course (although the latter will hopefully be taught in person).
Additional slides and videos
The following slides and videos are part of a set of “vignettes” about R (see here). They are required reading/watching for the course as they explain some mechanisms that your R assignments will need to use.