
Elements Of Statistical Learning: now free pdf - wglb
http://www-stat.stanford.edu/~tibs/ElemStatLearn//
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codedivine
May I also recommend a related book, also a free PDF:
<http://www.inference.phy.cam.ac.uk/mackay/itila/> "Information theory,
inference, and learning algorithms" by David Mackay. This book is not a pure
machine learning book but it is a very fun read.

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jimbokun
Anyone want to compare and contrast the two? Is one better overall? Do the
books have different strengths and weaknesses?

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pz
A lot of the Mackay book is on information/coding theory and while it will
deepen an existing understanding of ML, its probably a roundabout
introduction. ESL is a much better intro, especially for someone looking to
apply ML.

That said, it is meant for people who are comfortable with math/stats; its
much more statistics oriented than, say, Mitchell's book. But they do a good
job of explaining things in non-math language. This book does a good job of
exposing high level ML concepts (e.g. bias-variance tradeoff) but still
teaches a lot of the standard methods & tools.

These are actually two of my favorite books on the subject and I can't
recommend them both enough.

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cubedice
I noticed the R packages for various functions in the text can be found here
<http://www-stat.stanford.edu/~tibs/ElemStatLearn//Rfun.html> .

Their names are pretty catchy; I'll admit I _want_ to use 'gradient boosting'
and 'Lasso and elastic-net regularized generalized linear models'.

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jimbokun
We just covered Neural Nets in my machine learning class, and the professor
made some comments about how far a good name can take you. Neural Nets are
better than other learning algorithms in some ways and worse than others, but
the name gives you the sense that you are learning the secrets of how the
brain works. That may or may not be true, but it sure has served as good
branding for the Neural Networks algorithm.

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Eliezer
I have read this book and it is awesome.

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stevenbedrick
Agreed. It's great both as a reference and as a teaching/learning tool.

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paraschopra
I had read a couple of pages in the book on linear regression and was very
impressed by the way concepts had been explained. Although approach is very
mathematical, I will recommend this book to anybody serious about Machine
Learning

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Eliezer
The section on linear regression is extra awesome.

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simon_
several years ago, i took a class from prof tibshirani using this book and was
absolutely blown away. if you don't have a deep stats background (as i
didn't), you might find it scarily dense, but wow there's some good stuff in
there.

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subbu
I am starting to learn statistics. But I have zero knowledge in the subject. I
have picked up a copy of Headfirst Statistics. Is that a good one? Any
recommendations?

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lliiffee
"All of Statistics" by Larry Wasserman is very good. It is traditional, fairly
mathematical statistics, but intended for a general audience. (It is
particularly popular with computer science people.)

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jimbokun
It is also extremely concise, perhaps too much so occasionally for pedagogical
ease. It really does come pretty close to covering "all of statistics" in 442
pages, an impressive feat. But as you can imagine it certainly doesn't beat
around the bush much. So if you're looking for something with multiple
applications, covering the same material from different perspectives, etc., or
just want more hand holding, this may not be for you.

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briancooley
I love that the icon for technically difficult sections is Munch's _The
Scream_.

The visualizations are very nice.

Looks like a very useful text.

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arohner
Since this book is so math heavy, can anyone recommend a free pdf book that
can be used as a math intro for this? I like the book, but I get a little over
my head with the math in some sections.

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jimbokun
Don't know about math intro book, but I can't think of "free pdf" and machine
learning without also thinking about

<http://www.autonlab.org/tutorials/>

Probability for Data Miners, for example, has much of the basic math you need
for machine learning algorithms, for example.

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karthikm
Another useful resource might be the 20 part lecture series on Machine
Learning by Professor Andrew Ng for CS 229 in the Stanford Computer Science
department. Youtube playlist URL -
<http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599>

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yarapavan
Duplicate Post: <http://news.ycombinator.com/item?id=879423>

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nearestneighbor
Chapter 13 is the best!

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vdoma
Download link seems to be down :(

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pavs
<http://www-stat.stanford.edu/~hastie/Papers/ESLII.pdf>

