Linear Algebra - lectures + programming resources

[mrflibble]

While looking for something SVD related, stumbled across this one. Semi-random sampling of relevant links:

http://www.cs.utexas.edu/users/flame/laff/pfhp/week3-adding-cache-memory-into-the-mix.html
http://ulaff.net/
https://www.youtube.com/channel/UC-FlL9LPEipGNgaa1pS7aPg/videos
https://hornraiser.utexas.edu/project/16114/wall

IMO pretty useful, since it comes complete with the details that usually get glossed over in other courses.

[aix]

Since we're talking linear algebra, I really enjoyed Gilbert Strang's lecture series on OpenCourseWare.

Not sure if it covers the details you're after, but thought I'd mention it.

[mrflibble]

Since we're talking linear algebra, I really enjoyed Gilbert Strang's lecture series on OpenCourseWare.

Not sure if it covers the details you're after, but thought I'd mention it.

Yup, I like the lectures by Gilbert Strang as well.

Might as well list the linear algebra videolectures for 2005, 2011 and 2018:





And the OCW page for 2018:
https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/

And while we're at it, the linalg stuff by Steve Brunton (uni Washington) is also pretty good.
For those interested he also has a lot of stuff related to compressive sensing, and things like
Dynamic Mode Decomposition. Think "time evolution of compressed signal". More commonly known as
"How do I not melt my GPU while doing a time series of that compressive sensing thingy?".

Related playlist entries:




And the somewhat older material on DMD:

[Mattjd]

Strang and Brunton are both good.

If you like Brunton then you'll like Nathan Kutz from Uni Of Washington also. Brunton is part of Kut'z research group, as is Brunton's wife.

I've read a few of their papers, and can say their book on Dynamic Mode Decomposition is good.