VMLS by Stephen Boyd (also of Stanford) would be great background reading for the vector/matrix stuff (i.e. most of this course): https://news.ycombinator.com/item?id=18678314

I love this book. As opposed to the way that linear algebra is typically introduced, this book focuses on concrete applications (like text analysis, image/signal processing, finance, ML, etc.) and eschews more arcane concepts (like eigen). To me, building practical intuition is the best way to learn* the subject.

A more advanced but still accessible manuscript by Boyd et al: Generalized Low Rank Models (establishes connections between PCA/SVD and many other matrix factorization methods, and shows you how to roll your own): https://web.stanford.edu/~boyd/papers/pdf/glrm.pdf