

Free book on Bayesian machine learning by David Barber - markerdmann
http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf

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parrisj
Just a couple of notes 1) The linked version is out of date here the most
current version as of Nov 2011
<http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/211111.pdf>

2) @reader5000 It's legit he links to it from his homepage
[http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Main.Textbook)

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mjw
Having taken some courses from David and others at UCL recently, I'm a big fan
of this.

The Bayesian modelling perspective I think is very useful if you're interested
in machine learning as more than just a collection of clever algorithms and
optimisation techniques to throw at a problem and see what sticks. (Not that
this isn't useful sometimes...)

It provided a lot of motivation and unifying intuition for me anyway. The
elegance of having a nice statistical model doesn't come for free though,
there are some tricky computational issues associated with inference in many
Bayesian models. The book covers them in some depth and seems quite a useful
reference into the state of the art as well as a nice introduction to the
area.

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the_cat_kittles
W/r/t computing gnarly integrals, interested parties might appreciate pymc, a
python package that implements markov chain monte carlo methods to estimate
them

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mindcrime
On a related note, there are tons of gems like this out there, and there are a
handful of awesome sub-reddits dedicated to keeping lists of them:

<http://csbooks.reddit.com>

<http://physicsbooks.reddit.com>

<http://mathbooks.reddit.com>

<http://econbooks.reddit.com>

<http://eebooks.reddit.com/>

etc.

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solusglobus
The latest version of the book can be found at the author's page:- [1]
[http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Main.Textbook)
[2] Direct link: <http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/211111.pdf>

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reader5000
Assuming this is legitimately released (seems to be), authors who write and
release these books for free are heroes for those of us not currently
undergrads at Stanford etc.

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StavrosK
It is, I've taken this class and David Barber is releasing this for free.
That's the best thing I can say about that class, though.

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laacz
I'm sure, that because of TeX and stuff, it's popular among lots of people to
publish their free (or non-free) e-books as PDFs. Still, because of small
screen reading devices, it would be great if they could publish an epub also.
Or source. Or anything convertible to epub/mobi.

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jwr
This is one of the best resources for learning about Bayesian ML methods if
you need a gentle introduction. The only other book I found which was
similarly clear and well thought-out is Christopher Bishop's "Pattern
Recognition and Machine Learning".

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EmilStenstrom
I'm not sure I would call 600 pages of heavy math a "gentle introduction".

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grad_ml
Sorry to differ , but Barber's book is certainly gentler then Bishop. But
Bishop book is amazing , full of amazing insights !

