[gtani]

    machine learning techniques, which tend to be assumption-free 

ML should be a rigorous exercise in Bayesian and classical/frequentist stats, computational methods, dataset integrity, visualization etc, if you've been thru the texts by Murphy or Bishop. It often happens that people a couple years out of their last stats class only retain that high R-squared, p-, t- and f-values are what they're looking for, and heteroskedasticity and sphericity are just big words.

My evidence that ML is a rigorous exercise: the free texts listed (Barber, Mackay and Smola's are excellent, ESL not as accessible)

http://metaoptimize.com/qa/questions/186/good-freely-availab...

[scottedwards]

Thanks, @gtani, great resource. Yeah didn't mean to imply that ML techniques are free of ANY assumptions, just that several of the popular ones like logistic regression don't have distributional assumptions. (actually, I really want to understand the VC Inequality at some point, as it seems to allow us to make conclusions about out of sample error rates without depending on distributional assumptions)