What are some good sources for linear algebra for convex optimization and graph analysis? In Particular, is Gilbert Strang's MIT course suitable, or some other online course? I prefer online courses (video or lecture notes) to books because they're usually much better organized.
I'm at the undergrad level, but interested in doing research in machine learning and network theory, which use these two things respectively. I've taken one (basic, computationally-oriented: i.e. we didn't learn anything about basis, vector space or the meanings of linear mappings, but instead learned a lot about iterative methods Householder reflections) course on linear algebra, and all the time I find myself stymied by matrix formulations of optimization problems and assertions about eigenvalues of graphs.
I want to become fluent in it for these purposes - I don't plan to go further in the pure math direction, just engineering, so keep that in mind (though a few proofs and abstractions won't kill me, and are indeed welcome.
Side Question: Can you have a useful matrix of quaternions (since you can have complex as well as real matrices) or is that just a silly idea, for whatever reason?