Seyed Masoud Hosseini · Overview · Study log · Ideas · Transcript · RSS feed
Matrix Methods for Data Analysis & ML · Lecture 2 of 36 · 8:06
Interview: Gilbert Strang on Teaching Matrix Methods
Study guide
What this lecture covers
This is an interview, not a lecture: Sarah Hansen asks Gilbert Strang about the origins of 18.065 and his decision to teach it through student projects instead of exams. He reflects on how the course differs from a conventional linear algebra class and what he learned from running it this way.
Watching gives you context for the course's structure and philosophy rather than mathematical content: why projects replaced exams, and what Strang sees as his role as an instructor.
Key ideas
- Deep learning's dependence on linear algebra motivated Strang to build a new course around it.
- Projects instead of exams: 18.065 has no final exam; students propose a project by email, work on it independently, and some present it in the final weeks.
- Students often know more than the instructor about their chosen application, which Strang treats as a feature of the format, not a problem.
- Presenting is part of the learning: writing up and presenting a project is treated as valuable as the technical work itself.
- Teaching over grading: Strang states his main job is to teach or learn alongside students, not to grade them.
- Advice for new instructors: don't rush, don't try to cover everything, and stay with the class.
Before you watch
- This video works best after or alongside the course introduction, since it explains why the course is structured around projects rather than exams.
Check your understanding
- What led Strang to replace exams with a project-based structure in 18.065?
- How does Strang describe his role as an instructor in relation to grading?
- What logistical process did students follow to propose and complete their projects?
- Why does Strang consider project presentations valuable for students?
From the YouTube description
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Instructor: Gilbert Strang, Sarah Hansen
View the complete course: https://ocw.mit.edu/18-065S18
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63uMA4q8GaU6Eg5nzeOc8tx
In this video, Professor Gilbert Strang shares how he teaches his new course on matrix methods using a project-based approach.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
← Course Introduction to Matrix Methods for Data Analysis · Lecture 1: The Column Space of A Contains All Vectors Ax →
