5 ms·
I disagree re: ESL and RMS, but Harrell's book is superb. It's really more aimed at biostatisticians and clinicians, though. If the math in ESL gives you troub
by apathy 10y ago
I disagree re: ESL and RMS, but Harrell's book is superb. It's really more aimed at biostatisticians and clinicians, though.
If the math in ESL gives you trouble, you might prefer http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.p... (and I'm not just saying this because the first author was one of my advisers, although I do think that he and Daniela are particularly gifted teachers).
If the math in ESL is too trivial for you, there's always https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS_corrected_1.4.16.pdf https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS... , which covers some graphical modeling strategies in later chapters and even kicks the tires of the autoencoder (imho perhaps the greatest recent advance in neural networks for practitioners) along the way.
Koller's course and Ng's course are also good.
Ultimately I feel like you have to get the math right or you'll never acquire the intuition that helps you design your own approaches. But you also have to put in the work.
That reminds me, tibshirani's Stanford course (accompanies ISL and ESL) is terrific. Better than those other two, actually. I wish Harrell would offer one.
- blahi 10y agoYou disagree wit what regarding ESL and RMS? >Ultimately I feel like you have to get the math right or you'll never acquire the intuition that helps you design your own approaches. But what do you mean by that? Do I really need it if my applications are not as demanding as Netflix? I feel like many people consider anything less than phd-level understanding lol worthy, which is simply not true. Majority of analysts out there are doing just fine with canned procedures. Are there something like canned procedures for Bayesian Nets?
- apathy 10y agoI disagree that RMS is necessarily better than ESL in some meaningful way. The two are complementary. It's like saying a gravel truck is better than a motorcycle: it all depends on what you want to do with it. Re: "do I really need it?": hell if I know, I'm not you. But my assertion was specifically that if you want to design your own methods (i.e. do research) you need to understand what they are doing. This doesn't seem like a controversial position; an expert is simply a master of the fundamentals.
- blahi 10y agoBut I thought I made it clear that I am not looking to do research. I'm not even looking for state of the art performance. I am looking for that 30% of the skills which allow me to do 70% of the tasks. Like RMS. Because outside of Google/Microsoft/Amazon et al, domain knowledge beats superior math skills 10 out of 10 times.
- apathy 10y agoYou should do whatever you like, but remember that domain knowledge and math skills are not mutually exclusive. If the problem you need to solve for a major customer or project happens to be in that 30%, it may come in handy. Linear algebra and calculus (to a lesser degree) are foundational for a great many things. Got missing data? K-NN or nuclear norm matrix completion (or marginalizing over the rest) can help. Systems of differential equations? Use a matrix exponential. You are free to do whatever you like. A bus driver doesn't need to know how to rebuild an engine. But if you want to race cars you'll get a lot further if you do know how.
- blahi 10y agoIf I have missing values, I can use multiple imputation or even simple averaging and the hit I will take will be negligible. I simply do not work in the remaining 30%. I can tell if the work is going to be above my head and refuse the project in those cases. So after all this back and forth, I still don't know if there is a book similar to RMS in scope, for Bayesian Nets.
- apathy 10y agoTry http://www.r-bayesian-networks.org http://www.r-bayesian-networks.org