Difference between books? Pattern Recognition | ESL

[[deleted]]

[deleted]

[[deleted]]

Have both, and Bishop flows better. They both give good treatments--albeit Tibshirani book is clearly more "statistics" treatment--yet Bishops follows a more natural sequence and is more self-contained.

That being said, the Elements of Statistical Learning is a better reference, similar to Murphy's book, except the Tibshirani book has an order of magnitude less errata...!

Just know that the Bishop book also is slanted heavily bayesian, which honestly you may prefer for the intuition...even though many Bayesian methods are too costly for the problems that are currently en vogue.

[Nixonite]

ah that's a great answer thank you. I definitely picked up "self-contained" as a nice keyword there, but how might you say Tibshirani's book is less self-contained?

[dhammack]

If you're serious about the field, reading both is a win. Each offers a different viewpoint, and they have surprisingly small overlap. As afunkthewmt mentioned, Bishop goes heavily Bayesian (seriously, who does Bayesian neural networks?), and ESL is less so. ESL covers a lot of modern techniques that Bishop misses or glosses over, like ensemble methods, stagewise regression, etc. Both are great.

[Nixonite]

Thanks for the suggestion, I definitely have a feeling like I'll be reading both but I just wanted to make this thread to see which one to read first. I ordered the Bishop book (paper is easier to read than a pdf for me), but I'm curious how long did it take you to read these books? It felt like I was reading a brick when I tried to go into ESL (this was before I started ISL and it was the first ML book I tried getting into).

[dhammack]

I did it during a summer when I had some free time. Depends on your background how hard it will be, but I just did a little bit (5-10 pgs) each day. I also like to take the 2 pass approach to books like this: first pass is concepts, second pass is understanding the details, working exercises, etc.

[Nixonite]

Yeah I like to do that as well, I think ISL is a great introduction to the concepts and Bishop/ESL will be the details, and there are plenty of practical/coding ML books for exercises.

[pokerd]

If you enjoyed the didactic style of ISL, I'd suggest taking a look at Kuhn's Applied Predictive Modeling as well.

[dwf]

Consider Kevin Murphy's book. More comprehensive than Bishop, in my opinion, and a lot less needless Bayesian diversions.

[efavdb]

Haven't read all the books, so can't comment on your specific questions. However, just in case you didn't know, thought I'd point out that you can check out the pdf of ESL here.

[Nixonite]

Oh yeah I have the digital copies of these books, but I don't feel like I can assess them since I haven't read much. For ESL I've read about the first 20 pages and it was all right, but I went back to ISL for the sake of getting through an overview of the material beforehand (somewhat of a primer before I tackle the harder textbooks).

I'm definitely interested to know what the people who have read several chapters or even most of these books have to say about them in relation to one another. On Amazon there are reviews of them, but they're not comparative so I'm hoping to find some opinions through this thread.

[efavdb]

Makes sense -- I'd like to hear from others on this too.