Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Hacker News Reader

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

ocw.mit.edu

306 pointsericol21 comments

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

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

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!

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.

Would you recommend to get "Introduction to Linear Algebra, Fifth Edition" by Dr. Strang instead?

It’s a good book and actually serves as a good intro to this book. For Starngs learning from data book, you also need to know some convex optimization (not the difficult). Good luck!

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

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

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

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

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

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).

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

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.

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

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

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.

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).

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

Thank you OP. This looks like a wonderful resource.

Seems like an awesome course. Thanks for sharing

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