| I'm in my late twenties, and even though I did comm systems as a EE undergrad (lots of math), I still don't feel like I really have a deep understanding on probability/stochastic processes, differential equations, and complex analysis. www.betterexplained.com has some good tidbits for math. I've found MIT's opencourseware to be a pretty good help:
http://ocw.mit.edu/OcwWeb/web/courses/courses/index.htm#Math... Only the undergraduate courses tend to have video lectures though. The ones on linear algebra and diff eq are quite good. When I first learned matricies in high school, the teachers just went through the mechanics of how to manipulate them and how to calculate a determinant. It wasn't until years later, and when I started wtching these lectures that it crystallized for me what it actually meant. These are monthly lectures on math topics, which have been enlightening.
http://www.ams.org/featurecolumn/index.html If you like exploring, there's this:
http://www.jimloy.com/math/math.htm some free online texts
http://www.math.gatech.edu/~cain/textbooks/onlinebooks.html If you haven't had a course before, you can just follow the usual sequence of courses that high school students and college students take. calculus, multi-var calc, diff-eq, linear algebra, probabilty. And get a textbook and work through the problems. If you code, discrete math will probably help somewhere along the way. Probability/stat for machine learning. If you've already had the stuff before, It might help just to pick one small topic in a math field, like gradients in multi-var calc, and just focus on that for a bit, and inevitably, it'll mention some other math tool that you don't know about, and just follow your nose and interests. What I didn't learn until after I finished undergrad is that if you want to really understand conceptually what things mean in math, and not just how to manipulate symbols, there's no getting around working on problems paper (or matlab/mathematica) and just playing with it. Hope that helps. |