3 ms·

Where would you place "Elements of Statistical Learning" in relation to these, if you know?

by wjn0 10y ago

platz 10y ago
You want ISLr (http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/), not ESL. ESL is the prototype for the former.
- argonaut 10y ago
  Not sure why this is downvoted. ISLR is widely considered the "easier to read" / more pedagogical version of ESL. It's still mathematical but is closer to what the poster wants - a textbook that teaches them.
- wjn0 10y ago
  Interesting, thanks - I wasn't aware of this text. But according to your link, ESL isn't a prototype of ISL, but a more 'advanced treatment'. The R applications in ISL seem like they might be very useful, though.
  - argonaut 10y ago
    ISLR was written after ESL to be an easier to read version. I guarantee you will have trouble if you try to teach yourself ML with ESL.
davmre 10y ago
I haven't read it in detail, but my impression is that it is mathy, like Bishop, but focuses more on 'classical' frequentist analysis, whereas Bishop takes a more open-ended Bayesian perspective and covers important machinery like graphical models and inference algorithms that I don't think are in ESL. As a researcher I tend to prefer the Bayesian perspective in Bishop, because it gives you a unifying framework for thinking about building your own models and learning algorithms. But lots of people seem to respect ESL and speak very highly about it. It's probably valuable if you are implementing one of the methods it covers and want to understand that specific method in great depth.
- wjn0 10y ago
  I appreciate the comparison. My field is computational biology, and the latest edition of ESL has some specific, relevant examples, which is what drew me to it in the first place. I'm currently working on getting my statistics background up to par, and then I'll be choosing an ML textbook. However, your note about ESL focusing on frequentist models gives me pause about my original choice, as it would seem to me biological applications naturally lend themselves to Bayesian methods. I think in the end I'll have to crack open several books to decide. Thanks for the input.
  - davmre 10y ago
    You're welcome! FWIW I mostly agree with argonaut's point elsewhere in this thread - very few people successfully self-teach ML from a textbook alone. So whichever book(s) you choose, it might also be worth working through some course materials. I've already suggested Stanford's CS229 for solid foundations, but depending on your interests in bioinformatics, Daphne Koller's Coursera course on probabilistic graphical models (https://www.coursera.org/learn/probabilistic-graphical-models https://www.coursera.org/learn/probabilistic-graphical-model...) might be especially relevant. Koller literally wrote the book on PGMs, has done a lot of work in comp bio, and her MOOC is apparently the real deal: very intense but well-reviewed by the people that make it through.

Where would you place "Elements of Statistical Learning" in relation to these, if you know?

by wjn0 10y ago

Where would you place "Elements of Statistical Learning" in relation to these, if you know?

platz 10y ago

You want ISLr (http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/), not ESL. ESL is the prototype for the former.

argonaut 10y ago
Not sure why this is downvoted. ISLR is widely considered the "easier to read" / more pedagogical version of ESL. It's still mathematical but is closer to what the poster wants - a textbook that teaches them.
wjn0 10y ago
Interesting, thanks - I wasn't aware of this text. But according to your link, ESL isn't a prototype of ISL, but a more 'advanced treatment'. The R applications in ISL seem like they might be very useful, though.
- argonaut 10y ago
  ISLR was written after ESL to be an easier to read version. I guarantee you will have trouble if you try to teach yourself ML with ESL.

davmre 10y ago

I haven't read it in detail, but my impression is that it is mathy, like Bishop, but focuses more on 'classical' frequentist analysis, whereas Bishop takes a more open-ended Bayesian perspective and covers important machinery like graphical models and inference algorithms that I don't think are in ESL. As a researcher I tend to prefer the Bayesian perspective in Bishop, because it gives you a unifying framework for thinking about building your own models and learning algorithms. But lots of people seem to respect ESL and speak very highly about it. It's probably valuable if you are implementing one of the methods it covers and want to understand that specific method in great depth.

wjn0 10y ago
I appreciate the comparison. My field is computational biology, and the latest edition of ESL has some specific, relevant examples, which is what drew me to it in the first place. I'm currently working on getting my statistics background up to par, and then I'll be choosing an ML textbook. However, your note about ESL focusing on frequentist models gives me pause about my original choice, as it would seem to me biological applications naturally lend themselves to Bayesian methods. I think in the end I'll have to crack open several books to decide. Thanks for the input.
- davmre 10y ago
  You're welcome! FWIW I mostly agree with argonaut's point elsewhere in this thread - very few people successfully self-teach ML from a textbook alone. So whichever book(s) you choose, it might also be worth working through some course materials. I've already suggested Stanford's CS229 for solid foundations, but depending on your interests in bioinformatics, Daphne Koller's Coursera course on probabilistic graphical models (https://www.coursera.org/learn/probabilistic-graphical-models https://www.coursera.org/learn/probabilistic-graphical-model...) might be especially relevant. Koller literally wrote the book on PGMs, has done a lot of work in comp bio, and her MOOC is apparently the real deal: very intense but well-reviewed by the people that make it through.