About 2 years ago, I had an intense urge to learn linear algebra, statistics and probability in depth for much the same reasons as the author - to improve my machine learning and computer vision skills.
Really glad to see I'm not the only one.
I don't have any recommendations for linear algebra, but for stats and probability (which I always found intimidating in the past), Allen Downey's "Think Stats" and "Think Bayes" did the trick.
I'm enrolled in that class. In the introductory lecture Professor Klein shows a flowchart of the course†. This is the best course overview I've ever seen in any math class! I've spent /semesters/ in math sequences thinking "how the #@%! does this all fit together?!?!"
He says: "Don't try to read it all. It's a map... It's there to help you keep track of where you are and where we're going." Every professor should do that for their course. And every department should put up a big poster with something similar: these are the subject areas you will study, how they relate to each other, and the courses that cover those areas; if you choose this specialization these are the areas you'll focus on. Put-out a mind-map of the subject area that relates to the available courses—help students start building Elon Musks' mental-hyperloop / semantic-tree.
Really glad to see I'm not the only one.
I don't have any recommendations for linear algebra, but for stats and probability (which I always found intimidating in the past), Allen Downey's "Think Stats" and "Think Bayes" did the trick.