Mathematical Methods in Data Science

Sebastien Roch, Department of Mathematics, UW-Madison

This course on the mathematics of data has two intended audiences:

  • For math majors: it is meant as an invitation to data science from a mathematical perspective.

  • For (mathematically-inclined) students in data science (undergrad or grad): it can serve as a mathematical companion to machine learning and statistics courses.

Content-wise it is a second course in linear algebra, vector calculus, and probability theory motivated by and illustrated on data science applications. As such, students are expected to be familiar with the basics of those mathematical areas, as well as to have been exposed to proofs. Familiarity with data science is not required. However, while the emphasis is on the mathematical concepts, students enrolling in this class should be willing to learn a programming language. In Spring 2022, we will be using Python. Prior exposure to Python is preferable.

Course Information

Course number: MATH 535 - Mathematical Methods in Data Science
Semester: Spring 2022
Section 001 - time and place: MoWeFr 9:55AM - 10:45AM, INGRAHAM 120
Section 002 - time and place: MoWeFr 11:00AM - 11:50AM, VAN VLECK B239
Instructor: Sebastien Roch
Email: Use Piazza for all communications

Lecture notes

Lecture notes and notebooks can be found here.


The syllabus is on Piazza.

COVID Policies

Students enrolled in this class are expected to comply with the University’s current COVID rules that are maintained here:

Previous semesters

Some information from past semesters of MATH 535 can be accessed here.