Lecture Notes on Randomized Linear Algebra (2013) (opens in new tab)

This stuff is incredibly useful when dealing with large matrices. The idea is that an n-by-n matrix often doesn't contain n^2 pieces of independent information, but can be written a product of matrices of size at most n-by-r (for r << n). A famous example of this is the Netflix recommendation matrix. In this case, you can often avoid O(n^2) complexity by only dealing with such low-rank approximations.

It should be noted that this overview dates from 2013 and that a lot of new results have appeared since then. The author gives some good references in the abstract.

Related but different:

Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan: https://www.cs.cornell.edu/jeh/book2016June9.pdf

This is a very good and timely compilation of all the important topics!

I ask this earnest question because I have a deep interest in randomized linear algebra, random projections, 'sketching'/sampling, compressive sensing, etc.:

Do any of you use it in industry applications? If so, at a high level, how do you use it?

I know I'm asking a "I have a hammer and that is a nail"-type question, but I am interested in seeing "deployable" applications of these topics. I don't have any to report, other than academic ones.