Does set theory help understand machine learning or make new machine learning algorithms? When I was in a university, I didn't major in math but took some math classes. However, I dropped out of math classes pretty quick.
Some person recommended that I learn some set theory because it'll help me with Machine Learning . He recommended a book named Naive Set Theory by Halmos
Other people say it's not going to help much because set theory lives on a far higher abstraction level than mathematics used in machine learning do.
Since I don't know math well, I can't judge who's right.
Can anyone tell me how set theory is going to help me with machine learning ?

Machine learning, if you really want to understand it rather than just believe the algorithms do what they are supposed to do, requires quite a heavy dose of mathematics. That mathematics is written in a language and that language is set theory a la Halmos (i.e., the naive kind). If you wish to properly understand the mathematics involved in ML, then it is a good idea to have a good grasp of the underlying universal language which is set theory. Halmos' book is quite short and does not go to the very high level of abstraction that set theory proper goes to. If ML is your goal, I'm would not say Halmos is a necessity, but it is certainly a worthwhile reading. Just don't expect it to translate into any algorithm. 
