layout: default title: 18.065 parent: Spring 2023 nav_order: 1 —

‘18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning’

Table of contents

  1. Course Info
  2. Realistic Prerequisites
  3. Subject Matter
  4. Course Staff
  5. Lectures
  6. Problem Sets
  7. Exams
  8. Resources
  9. Grading
  10. Advice to Future Students

Course Info

Class Size 70
Hours/Week 11.2 (28 responses)
Instructors Steven Johnson
Overall Rating 6.2/7.0

Realistic Prerequisites

  • People recommend having taken 18.06 as a hard prerequisite.
  • The class does review linear algebra in the beginning but at a faster pace.

Subject Matter

  • Students described the content as useful, foundational, broad, practical, with a mix of theoretical and applied content.
  • The ML part was covered in depth in certain areas.

Course Staff

  • There were no TAs/LAs, but the professor was helpful, approachable, and enthusiastic in office hours, and gave more detailed answers in office hours compared to Piazza.


  • The professor mostly wrote notes as he taught, sometimes having demo code and slides.
  • The students thought the lectures were the most helpful and the most referenced resource.
  • Some students also found the textbook helpful.

Problem Sets

  • There were biweekly problem sets which students spent 4-10 hours on.
  • Students thought the problems were relatively straightforward and canonical, though some students thought they were also interesting and fun.


  • There were no exams, but a final project.
  • One student thought that the final project was the best part of the class.


  • The professor posted handwritten notes to canvas, and people found the textbook very helpful.
  • There was an active Piazza.
  • The textbook was less relevant later in the semester.


  • Students felt that the grading was lenient but slow.

Advice to Future Students

  1. ”Brush up on linear algebra skills if it has been a while since taking a course.”
  2. “Find a good group for the psets otherwise you will give up.”