Click here to see the Fall 2025 course website.

Mathematics for Machine Learning 🧠

EECS 245 at the University of Michigan Winter 2026 Preview

Are you a computer science, data science, or statistics major who needs to take a linear algebra course and is interested in machine learning? Take EECS 245 in Winter 2026!

  1. Content
  2. Credit
  3. Prerequisites
  4. Workload and Course Evaluations
  5. Logistics
  6. Questions?

Content

Click to see the official catalog description.Mathematical foundations of machine learning and artificial intelligence, focusing primarily on linear algebra, along with select topics from calculus and probability. Topics include matrices, vectors, projections, spans, least squares, and eigenvalue and eigenvector problems. Includes mathematical theory as well as application to concrete data sets in a modern programming language.

Check out eecs245.org, which contains links to our brand-new interactive course notes, lecture recordings, homeworks, labs, and exams.

Credit

As of November 10, 2025, the course counts for:

  • The linear algebra requirement for:
    • Computer Science majors (CS-Eng and CS-LSA*).
    • New! Data Science majors (DS-Eng and DS-LSA).
    • New! Statistics majors.
    • New! Artificial Intelligence minors.
  • The linear algebra prerequisite for:
    • EECS 442, EECS 445, EECS 448, EECS 474, EECS 476, and CSE 576.
    • STATS 413.

If you take the course in Winter 2026, you’ll need to submit petitions to get into the above courses, but rest assured, we have agreements in place with the relevant departments and instructors to get you into the courses you need. We will also share your information with the CS/DS/Stats advisors to get you linear algebra credit.

*CS-LSA students don’t have a linear algebra requirement, but this course fits in the linear algebra “bucket” for the second math course; see here for details.

Prerequisites

This course has a math prerequisite and a programming prerequisite.

  • EECS 203 or Math 116 (or Math 215, 216, 275, 285, or 295), i.e. some math course after Calculus 1.
  • Some 100-level programming class (e.g. EECS 183, ENGR 101, ROB 102, or similar).

Workload and Course Evaluations

This course has 11 weekly homeworks, weekly in-person labs, 2 non-cumulative midterm exams, and a cumulative final exam, which allows you to get back some of the points you lost on the midterms.

We surveyed current students to get their estimates for how much time they’ve spent on each homework, and here were the class averages:

Hours Per Week

Our target is that the course takes a similar amount of time per week as EECS 203. We’re aware of the fact that recent homeworks have been more time-consuming than that target and will work to address this next semester.

You can also find the official midterm course evaluations here.

Logistics

There is one live lecture section, on Mondays and Wednesdays from 12-1:30PM in 1013 DOW. Students in both sections (001 and 002) can attend in-person or access recordings.

There are four lab sections (see here for the full schedule); make sure to enroll in a lab section you can attend regularly, since lab attendance is a part of your grade.

Questions?

If you have any questions about the course, don’t hesitate to contact Suraj Rampure at rampure@umich.edu. I’m happy to meet with students 1-on-1 to answer any questions they may have about the course.