Re:Ask HN: What maths are critical to pursuing ML/AI?
by mtzet on Monday August 28, 2017 @03:24PM
(#15119301)
The relationship is that measure theory provides the theoretical framework for making probability theory rigorous.
The only formal prerequisite for learning measure theory is that you should know series and sequences. For a reference, I'm not so sure, maybe Halmos's book. The important parts are probably:
- Monotone convergence theorem
- Dominated convergence theorem
- The construction of the Lebesgue integral
- Fubini's theorem and Tonelli's theorem
I would probably try not to get bogged down in details of construction of measures (unless you like that) and take the Lebesgue measure (essentially length) as given. Also check out the Radon-Nikodym theorem which states that we can always (ish) work with density functions.
The only formal prerequisite for learning measure theory is that you should know series and sequences. For a reference, I'm not so sure, maybe Halmos's book. The important parts are probably:
- Monotone convergence theorem
- Dominated convergence theorem
- The construction of the Lebesgue integral
- Fubini's theorem and Tonelli's theorem
I would probably try not to get bogged down in details of construction of measures (unless you like that) and take the Lebesgue measure (essentially length) as given. Also check out the Radon-Nikodym theorem which states that we can always (ish) work with density functions.