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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.




For LA a great place to start is http://codingthematrix.com/

For programmers with less math experience it serves as a perfect transition with which to then go into more theoretical aspects of LA.


A Coursera course based on that textbook, taught by its author, just started a couple days ago.


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.

†: http://picpaste.com/Screen_Shot_2015-02-05_at_1.58.47_AM-AnX... available on the course website: http://cs.brown.edu/courses/cs053/current/graphical-outline.... ††: https://www.reddit.com/r/IAmA/comments/2rgsan/i_am_elon_musk...


For linear algebra, I'm a fan of Hoffman & Kunze. The clearest, most useful book on the subject I've used: http://www.amazon.com/Linear-Algebra-2nd-Kenneth-Hoffman/dp/...




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