Youtube playlist for the course:

https://www.youtube.com/playlist?list=PLdPQZLMHRjDK8ZbLIcq1Q...

Materials on Github:

https://github.com/michiganrobotics/rob101

I took this course 3 years ago. I found it fast-moving, and it focused a lot more on applications than fundamentals, which meant it was more wide than it was deep. This didn't turn out so well when I decided to study ML later and needed stronger linear algebra fundamentals, but it was a fun course. There were a couple interesting course projects, one of which was using linear algebra to balance a (simulated) 2D robot.

Man this would have been nice when I was in school.

For some reason linear algebra still isn't part of standard Mechanical Engineering course load (Calc 1, 2, 3, DiffEq) which made life extremely difficult in some of the later classes. I remember spending weeks brute forcing a lot of things that would have been trivial with a little bit of matrix math.

I took a superficially similar class as a 400 level elective but it assumed everyone already knew linear algebra going in, and it was a disaster.

Chapter 13 of the textbook was added in January 2022. It covers separating hyperplanes, signed distance to a hyperplane, Max-margin Classifiers, a remark on Soft Margin Classifiers, and the Orthogonal Projection Operator. The additional material was added to support EECS 445, Machine Learning at Michigan.

For folks interested in 101 on linear algebra - I highly recommend book "Linear Algebra: Theory, Intuition, Code" by Mike X Cohen.

After trying a couple of courses and books, I liked it the most because it gives a pretty deep overview of the concepts, alongside the numpy and matlab code, which I found refreshing.

It's has good amount of proofs and has sections designed to build your intuition, which I really appreciated.

I taught numerical linear algebra in grad school and was really frustrated that even the applied math department took so long to build up to solving linear systems and eigen-decompsotions. The ordering of the material in the textbook is great, focusing on algorithms and decompositions.

Chapter 13 of the textbook was added in January 2022. It covers separating hyperplanes, signed distance to a hyperplane, Max-margin Classifiers, a remark on Soft Margin Classifiers, and the Orthogonal Projection Operator. The material was added to support EECS 445, Machine Learning at Michigan.

MATH 214 (intro to Linear) was the least enjoyable class during my undergraduate at Umich. This seems like a better intro.

I love Linear Algebra. I took it in college almost 20 years ago and I still use it everyday. The higher level maths almost broke me academically. And it was a course in LA that really kept my head in the game. Even now, when I'm talking to students I try and encourage them to take the class if it's available.

What's the best online credential for doing linear algebra? I like to do some self-studying but also, I'd like some form of "evidence" that I actually know my stuff and don't have t constantly explain that I do

I like Lay, it's one of the few math books anyone can read cover to cover, prove every statement and solving every problem, with no experience. It's like the Thomas' calculus equivalent linear algebra. If you do the work you'll get an easy A and will have built a great foundation for further engineering or theoretical study.