by txmjs on 3/2/18, 9:20 PM with 12 comments
by jfaucett on 3/3/18, 2:26 PM
For me, the biggest aid was finding good books, ones with exercises and that explained the material very well. Then it was just a matter of reading, actually doing all the exercises, and struggling with the material until I could fully understand it; then I moved on to the next chapter/book/etc.
I've worked through a lot of books by now but here's the short list of ones that I think are great for getting started(especially, if you do what I described above), also when these have solutions manuals I would advise getting them as well so you can ensure you understand how every problem works.
1. Calculus 4th ed. by Smith and Minton
2. Introduction to Linear Algebra (Gilbert Strang)
3. Introduction to Probability Theory (Hoel, Port, Stone)
4. Discrete Mathematics and its applications (Rosen)
5. Introduction to Automata Theory, Languages, and Computation (Hopcroft)
6. Introduction to Algorithms (Cormen, Leiserson, Rivest, Stein)
It will take you at least a year if not two to work through all these in your spare time, but the advantage is that after that you'll have the skills to be able to approach just about any topic in computer science (even highly theoretical ones) and not have much difficulty understanding them (at least that was my experience).
by nextos on 3/3/18, 4:27 AM
Alternatively, you can start by studying any of the alternative foundations to mathematics (set theory, logic, etc). Perhaps from a toned down alternative to Sørensen & Urzyczyn. A beautiful and very modern way to do this for a CS student would be to marry it with a course on automated theorem proving (using Isabelle or Coq). Any book suggestions welcome.
by ezekg on 3/3/18, 2:02 AM
by BiancaDelRio on 3/2/18, 9:30 PM
https://web.stanford.edu/class/cs103/notes/Mathematical%20Fo...
by junk_f00d on 3/7/18, 3:26 AM
by seagullz on 3/3/18, 5:07 AM
Given that linear algebra has become so vital lately (as others remarked already), the following new book appears better than the lot from an applied viewpoint: https://web.stanford.edu/~boyd/vmls/vmls.pdf