Research Blog: Machine Learning Book for Students and Researchers(googleresearch.blogspot.com)
googleresearch.blogspot.com
Research Blog: Machine Learning Book for Students and Researchers
http://googleresearch.blogspot.com/2012/08/machine-learning-book-for-students-and.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+blogspot%2FgJZg+%28Official+Google+Research+Blog%29&utm_content=Google+Reader
6 comments
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.
And this one is not in Google's cache. I've taken to reading the cached version of many blogspot pages, for this reason -- I have a browser extension that makes that just a couple of clicks to accomplish. Maybe Google has caught on to this.
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...
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...
Completely agreed. I think it's fine to expect now that it wont render pretty, some divs may be in the wrong place... but shit should load.
and it breaks the back button
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 the algorithms you're using or develop new algorithms yourself then Prof Mohri's of rigor is very useful.
I took Prof. Mohri's class this spring, and we actually used a draft version of this book. I agree with you that it's very theoretical, but Bishop, Mitchell, and the other ML resources don't address learning theory. VC-dimension isn't for everyone, but if you need to know about it, this is the book you want to use.
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).
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).
I wish the window for fixing a comment was a bit longer...
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/...
The Bishop book is the most popular though: http://www.amazon.com/gp/product/0387310738/ref=pd_rvi_gw_2/...
The Elements of Statistical Learning, by T. Hastie, R. Tibshirani and J. Friedman [1] is also a very good one. Plus, the book is freely available on the authors' website.
[1] http://www-stat.stanford.edu/~tibs/ElemStatLearn/
[1] http://www-stat.stanford.edu/~tibs/ElemStatLearn/
So this is a common misconception about the text (and Prof. Mohri's NYU class). In this case, "foundations" does not mean this is an introductory course.
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.
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.
I have been putting off buying that book because of the price, maybe I should check the library.
Does anyone know if the table of contents is somewhere online?
One of the authors (Mohri) teaches a course called Foundation of Machine learning. Details are here[1] and may give an indication of the books' contents.
[1] http://www.cs.nyu.edu/~mohri/ml10/
[1] http://www.cs.nyu.edu/~mohri/ml10/
Use this link instead:
http://www.cs.nyu.edu/~mohri/ml12/
It's the last class he taught before the book was published. I was in the class, and can confirm the lecture outline closely matches the book's contents.
http://www.cs.nyu.edu/~mohri/ml12/
It's the last class he taught before the book was published. I was in the class, and can confirm the lecture outline closely matches the book's contents.
There's a Kindle edition at Amazon, so you can possibly get the TOC by getting the free Kindle preview from Amazon.
Right, without at least a TOC you can't tell what is in the book.
Why is this news?
See my comments above. The book fills a large gap created by the popular ML textbooks (namely, none of them review learning theory).
http://www.nytimes.com/2012/02/12/sunday-review/big-datas-im...
http://www.nytimes.com/2012/08/12/business/how-big-data-beca...
http://www.nytimes.com/2012/08/12/business/how-big-data-beca...
So this machine learning textbook is applied specifically to big data, in a way that is novel and new?
When will this be on MegaUpload?