Viewing profile — davidsrosenberg

Interesting -- if you've got a link, please post it.

So the extrapolation-type problem you describe (an input not near any of your training examples) is an issue. Unless you have a world model you believe in (i.e. you've done some sc…

I also really like the Abu-Mostafa course from caltech you link, and their book. If you want to get a taste of generalization bounds and statistical learning theory (e.g. VC dimens…

I imagine Breiman was just talking about bagging-style parallel ensembles, when he was talking about variance reduction, not boosting-style sequential ensembles. Not long before he…

Funny - I had the same thoughts and Boyd and Vandenberghe’s book, which is why I compiled this “extreme abridgment” for what you need for the class: https://davidrosenberg.github.i…

Hehe ok —- I also love Breiman’s Probability book. It’s really a standout on Ergodic theory. And Breiman et al.’s book on Trees is surprisingly rich, talking about all sorts of stu…

Yes, of course. A “Bayes prediction function” has nothing to do with Bayesian. Bayes had a lot of things named after him ;)

Nice you just provided the solution to Homework 1, Problem 3.1 ( https://davidrosenberg.github.io/mlcourse/Homework/hw1.pdf ).

You seem to have a preference for an approach in which you assume certain things are true about the world (e.g. y is a linear function of x), and then you derive some optimal predi…

Here are some of the things that I think are distinctive about the class (although certainly all of these are taught in some other class somewhere): discussion of approximation err…

This course is complementary to Mohri's excellent book and course. Many students at NYU take both courses, in either order ( https://davidrosenberg.github.io/ml2018/ and https://cs…