2013-10-06, 02:35 PM
I'm a CS major. I think I've taken more math than most CS majors because I used to think I liked theoretical math and physics and because I'm into data science and so I've taken a fair bit of probability/stats and such.
For what it's worth, I would argue that theoretical computer science (algorithms, formal languages, P vs NP, etc) is also a kind of math, even if it's very different from, say, calculus. I've taken two algorithms classes as part of my major, and I'm considering at some point taking theory of computation which sounds fascinating though useless.
I had the same experience when I took theoretical linear algebra (a class for math majors, which is even worse in this respect than the applied sort of linear algebra for engineering majors and such -- all you do is prove things that don't seem to have any bearing to anything you care about).
I found that I appreciated it a lot more once I learned about other things that used it, particularly quantum mechanics, but also machine learning, and digital communications. The problem with linear algebra is that it's dry to learn all by itself, but it's hard to learn about its applications without knowing it first.
For what it's worth, I would argue that theoretical computer science (algorithms, formal languages, P vs NP, etc) is also a kind of math, even if it's very different from, say, calculus. I've taken two algorithms classes as part of my major, and I'm considering at some point taking theory of computation which sounds fascinating though useless.
Grey Wrote:Yes, I recall covering something like that towards the end. It's not like I don't see why Linear Algebra is useful, or some practical applications. I just meant that I, specifically, don't (perhaps only currently) have a use for linear algebra, and I dislike it because it doesn't really interest me, not that I find anything bad about it. I guess I exaggerated my dislike of it, but it is the math I like the least.
I had the same experience when I took theoretical linear algebra (a class for math majors, which is even worse in this respect than the applied sort of linear algebra for engineering majors and such -- all you do is prove things that don't seem to have any bearing to anything you care about).
I found that I appreciated it a lot more once I learned about other things that used it, particularly quantum mechanics, but also machine learning, and digital communications. The problem with linear algebra is that it's dry to learn all by itself, but it's hard to learn about its applications without knowing it first.