>>12511611>>12511648this is 100% not true. OP don't fucking listen to this advice. Math people don't actually use stats, so they're unqualified to give training advice for someone like you. Analysis and measure theory are great, and if you want to do probabilistic ML research, it's definitely worth learning, but it's completely demented to think that you need 2+ semesters of analysis just to start with probability.
Almost nobody (and certainly absolutely nobody in stats departments) learns measure theoretic probability + stats before knowing a fairly hefty amount of both subjects first. If you have calc under your belt, learn the standard probability/math stats curriculum. Here are some books:
>under grad probRoss and Grimmett's intro books are both good. Grimmett's book on prob/stochastic processes is a great step up after this, and a measure theoretic probability book like Pollard's or Durrett would be the natural 3rd book to read, if you end up studying analysis and want to go deep.
>stats Cassella is the standard, but maybe there's something better.
>both in oneIf you want to speed run through probability, and you want a terse exposition of stats, Wasserman's All of Statistics is the way to go.
Finally, if you want to do ML research, you can't beat Michael Jordan's list.
