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They funny thing is that all this is clearly spelled out in PAC-Learning and Statistical Learning Theory, but the trend in most machine learning courses is to a
by YeGoblynQueenne 3y ago
They funny thing is that all this is clearly spelled out in PAC-Learning and Statistical Learning Theory, but the trend in most machine learning courses is to avoid all that hairy stuff, and concentrate on the practical tasks of training classifiers and using popular libraries and so on.
Btw, in the above comment:
FID = Fréchet inception distance
NLL = Negative Log Likelihood
- godelski 3y agoYeah this is what bugs me. If these people took a single stats class it would be drilled into them. Doesn't matter if ISLR, PAC-LSLT, SR, or ROS, it will be there. But it isn't. I push for as much of this as I can when I teach my ML course but my advisor pushes back for being "too mathy." I swear, ML people are adverse to math (though they like adding meaningless math equations to papers to make them look technical, but that is probably aligned with the former comment). As the current environment stands, I do not think many ML people understand concepts like data leakage, Bayes, or even likelihood. A problem is that popular way to teach ML is like how you teach software: coding. This doesn't work when you need statistical theory to understand your results.