| If the original poster is a computer person, I'd recommend some changes to this list. This is "Math for physics" but "Math for Computer Science" is rather different, and might be more interesting. I'd recomment calculus up to integration. Don't worry about integration tricks, except for integration by parts (the most important formula in mathematics) and u-substitution. All the other integration tricks are pointless crap used to fill up time in calc classes. Vector math is useful if you like either computer graphics or physics, but is not crucial. On the other hand, everyone should know probability, even the purest mathematicians. Just don't try to learn it out of an "Introduction to Probability and Statistics for Engineers" book, all such books should be burned. Real/functional analysis would also be useful to better understand probability. I'd also suggest combinatorics/graph theory, and perhaps the theory of automata. That's edging towards computer science, but it is a fundamentally mathematical topic. Also, it will be very slow going. It's not like picking up another computer language/framework; it's even harder than Haskell. I've have a Ph.D. in mathphys/num analysis, but it still takes me a long time to push through an introductory textbook in a field too far removed from my own. For instance, I sat through 4 semesters of abstract algebra (3 at the grad level), and I still don't understand it. Don't get discouraged. |