davidsrosenberg

5 ms·

1. 1.▲
   by davidsrosenberg 8y ago

   Interesting -- if you've got a link, please post it.
2. 2.▲
   by davidsrosenberg 8y ago

   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 science -- not just statistics), hard to know if your pre
3. 3.▲
   by davidsrosenberg 8y ago

   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 dimension), he gives the gentlest introduction I've seen.
4. 4.▲
   by davidsrosenberg 8y ago

   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 died, he was still actively trying to figure out why AdaBoo
5. 5.▲
   by davidsrosenberg 8y ago

   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.io/mlcourse/Notes/convex-optim...
6. 6.▲
   by davidsrosenberg 8y ago

   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 stuff besides trees.
7. 7.▲
   by davidsrosenberg 8y ago

   Yes, of course. A “Bayes prediction function” has nothing to do with Bayesian. Bayes had a lot of things named after him ;)
8. 8.▲
   by davidsrosenberg 8y ago

   Nice you just provided the solution to Homework 1, Problem 3.1 ( https://davidrosenberg.github.io/mlcourse/Homework/hw1.pdf ).
9. 9.▲
   by davidsrosenberg 8y ago

   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 prediction function, based on that assumption, under some definit
10. 10.▲
    by davidsrosenberg 8y ago

    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 error, estimation error, and optimization error, rather than th
11. 11.▲
    by davidsrosenberg 8y ago

    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.nyu.edu/~mohri&#x