As someone who did graduate work in Bayesian methods for a statistics master's degree, I take offense to saying machine learning is not a concern of statistics and data mining (but not really)! The hesitance towards Bayesian methods seems more related to the discipline, and it seems that places that call what they do "machine learning" tend to be less hostile towards the explicit subjectivity of Bayes (I would highlight the word "explicit" in that sentence--Frequentism has it's fair share of subjectivity as well).

There was a great post on Stats.SE a few years ago about the difference between statistics and machine learning[1]. Leo Breiman once argued that statistics tends to focus more on model fitting and checking, while machine learning looked at prediction accuracy. The exchange between Andy Gelman and Brendan O'Connor is pretty funny. It has been my personal experience however that many people that apply a method that they brand as "machine learning" are not as bothered with assumptions as my fellow conservative statisticians.

But statistics and machine learning are quite similar in foundation. Barring the differences in terminology, as a professional statistician, I find I have as little difficulty read machine learning papers and algorithms as I do reading statistics ones.

http://stats.stackexchange.com/questions/6/the-two-cultures-...