[cbHXBY1D]

And what about after that? In my experience at least, I can highly recommend Stanford's EE364a Convex Optimization. http://web.stanford.edu/class/ee364a/

When I was an undergrad at Berkeley, one particularly well known research lab would only look at your resume if you took that course online. Warning: that class is not for the faint of heart. And be good at linear algebra.

[hackermailman]

CMU also has a convex optimization course w/open recorded lectures on youtube http://www.stat.cmu.edu/~ryantibs/convexopt/

None of these classes are for the feint of heart if you're actually enrolled in them and operating under crazy deadlines with thousands of dollars on the line. This is clearly for our casual interest and hacking around with ML research hobby.

[graycat]

Stephen Boyd's Convex Optimization,

http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf

is gorgeous material. Large parts it are from theorems of the alternative, linear programming, Jensen's inequality, R. T. Rockafellar, etc., but Boyd makes it all a clean whole.

That's the upside! The downside is that, IMHO, rarely does any one person need more than a small part of the whole book, and the parts that someone needs are likely also available in the subject they are working with.

So, at least download Boyd's book and use it for reference: If ever have a question about convexity, then start with Boyd!

[davidsrosenberg]

Funny - I had the same thoughts and Boyd and Vandenberghe’s book, which is why I compiled this “extreme abridgment” for what you need for the class: https://davidrosenberg.github.io/mlcourse/Notes/convex-optim...

[iampno]

Nice

[throwawaymath]

Which research lab was that?