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

by Anon84 • 14 years ago

67 21 14 years ago

googleresearch.blogspot.com

cmansley• 14 years ago

Does anyone know if the table of contents is somewhere online?

andyjohnson0• 14 years ago

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/

achompas4• 14 years ago

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.

mturmon• 14 years ago

Right, without at least a TOC you can't tell what is in the book.

tzs• 14 years ago

There's a Kindle edition at Amazon, so you can possibly get the TOC by getting the free Kindle preview from Amazon.

iskander2• 14 years ago

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.

iskander2• 14 years ago

I wish the window for fixing a comment was a bit longer...

achompas4• 14 years ago

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).

dude_abides• 14 years ago

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/...

exg• 14 years ago

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/

manaskarekar• 14 years ago

I have been putting off buying that book because of the price, maybe I should check the library.

achompas4• 14 years ago

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.

imurray• 14 years ago

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.

codemac• 14 years ago

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.

pasbesoin• 14 years ago

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...

executive• 14 years ago

and it breaks the back button

JoelMcCracken2• 14 years ago

Why is this news?

larrydag• 14 years ago

http://www.nytimes.com/2012/02/12/sunday-review/big-datas-im...

http://www.nytimes.com/2012/08/12/business/how-big-data-beca...

JoelMcCracken2• 14 years ago

So this machine learning textbook is applied specifically to big data, in a way that is novel and new?