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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
by 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 agoYou'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.