Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

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

by ericol • 7 years ago

306 21 7 years ago

ocw.mit.edu

AceJohnny2• 7 years ago

Ooh this is interesting. His Linear Algebra book was my favorite source on the topic back in college (though I hear people have alternate preferences).

shstalwart• 7 years ago

I really like Axler's Linear Algebra Done Right. It's all conceptual (i.e., proofs), and doesn't focus on computation at all.

akulkarni• 7 years ago

18.06 Linear Algebra was a fun class. Can’t believe he is still teaching.

axiom922• 7 years ago

Thanks for sharing this. It's based on his new book: https://www.amazon.com/Linear-Algebra-Learning-Gilbert-Stran...

baking• 7 years ago

I bought this book a couple of weeks ago and I've been waiting eagerly for these lectures to be posted.

stevesimmons• 7 years ago

Can anyone who has seen the book comment on how good it is?

There are just two reviews on Amazon, one 5* and one 1. The 1 said "The book resembles a set of somewhat incomplete and somewhat disorganized lecture notes prepared by a course instructor as a rough outline of the content of his course."

That put me off buying it. So more data points would be helpful!

nagVenkat2• 7 years ago

I have the book and I wish I didn’t buy (it’s an expensive book). You need to be already pretty good with Linear algebra and most of the material looked like cliff notes of various linear algebra and continuous optimization topics. I still plan to go through it but mildly disappointed.

pixelperfect• 7 years ago

Thanks for sharing. I hope at age 83 I can be as mentally sharp as Gilbert Strang is.

axiom922• 7 years ago

Depending on how old you are now, by the time you're 83, you could find yourself being a part of the new "middle aged" group :-) (i.e., we could end up inventing ways of increasing our lifespan dramatically).

adamisom• 7 years ago

or there could be nobody around to make the observation that there's nobody around, if any existential risks actualize

bigred100• 7 years ago

Thank you OP. This looks like a wonderful resource.

AareyBaba• 7 years ago

Playlist on Youtube https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIU...

yannis• 7 years ago

Notable is Alan Edelman's tip using Julia at the 36th lecture.

jay754• 7 years ago

Seems like an awesome course. Thanks for sharing

aj7• 7 years ago

Looks like state variables and signal processing, now mature with LabView and MatLab plugins and standard VLSI solutions being deemphasized in favor of machine learning and data science.

charlysl• 7 years ago

Labs, although mentioned in the syllabus, are not in the OCW materials. This is the best I could find (4 labs in Julia, Julia notebooks, and Matlab): https://github.com/dkout/18.065

usgroup• 7 years ago

You’re doing SVD by lecture 3 (typically the last thing you do on a LA II course), so brace yourself. If you’re not already VERY good at LA I’d wait until you are before attempting this.

anilakar• 7 years ago

Not trying to mock anyone, but linear algebra is basic university level math.

vonmoltke• 7 years ago

If you had the course. It was not in the engineering curriculum at my university. We got four semesters (effectively) of calculus instead.

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

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