11–20 of 22 posts
My favorite introductory textbook on machine learning is the Tom Mitchell book: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007... The Bishop book is the most popular though: http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
Aside: I find it ridiculous that I can't read a blog post, a little bit of text and an image, without enabling javascript on two domains. I know that a bunch of you will think only masochists use NoScript, but the page is slow to appear and needlessly complicated. If there's anyone at Google who could help your company set a better example, please nudge the right people.
EDIT: My mistake; I just had to strip off the bazillion query parameters.
Google cache of the post:
http://webcache.googleusercontent.com/search?q=cache:http%3A...
Aside: I find it ridiculous that I can't read a blog post, a little bit of text and an image, without enabling javascript on two domains. I know that a bunch of you will think only masochists use NoScript, but the page is slow to appear and needlessly complicated. If there's anyone at Google who could help your company set a better example, please nudge the right people.
I haven't seen this book before but I did take Prof. Mohri's class a few years ago. His material is great but be warned that is's biased heavily towards theory (proofs and bounds instead of the more common/practical "rules of thumb"). You can get a lot done using machine learning tools having only a superficial familiarity with VC dimension, complexity bounds, etc... If however, you want to get deeper insight into th…
Does anyone know if the table of contents is somewhere online?
I haven't seen this book before but I did take Prof. Mohri's class a few years ago. His material is great but be warned that is's biased heavily towards theory (proofs and bounds instead of the more common/practical "rules of thumb"). You can get a lot done using machine learning tools having only a superficial familiarity with VC dimension, complexity bounds, etc... If however, you want to get deeper insight into th…
There's also the added bonus of this text coming from Prof. Mohri et al.'s experiences at Google, so there's a lot of discussion about online algorithms and ranking (which you don't see in many other places).
Does anyone know if the table of contents is somewhere online?
My favorite introductory textbook on machine learning is the Tom Mitchell book: http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/007... The Bishop book is the most popular though: http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
Rather, the class and text provide mathematical foundations for understanding the error bounds and growth complexity of various learning algorithms. So you'll be workin with convex optimization, reproducing kernel Hilbert spaces, and Rademacher complexity--definitely not "introductory" in the least!
It's a completely different beast from Mitchell, Bishop, or EoSL (which I'm studying right now!), so I'm not sure comparisons are valid. It also fills a prominent gap in the ideas reviewed by the popular ML texts.