

Free eBooks On Machine Learning - mhausenblas
http://efytimes.com/e1/fullnews.asp?edid=121516

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phoen
The LION Way: Machine Learning plus Intelligent Optimization
[http://www.e-booksdirectory.com/details.php?ebook=9575](http://www.e-booksdirectory.com/details.php?ebook=9575)

A Course in Machine Learning
[http://www.e-booksdirectory.com/details.php?ebook=9395](http://www.e-booksdirectory.com/details.php?ebook=9395)

A First Encounter with Machine Learning
[http://www.e-booksdirectory.com/details.php?ebook=8818](http://www.e-booksdirectory.com/details.php?ebook=8818)

Bayesian Reasoning and Machine Learning
[http://www.e-booksdirectory.com/details.php?ebook=5283](http://www.e-booksdirectory.com/details.php?ebook=5283)

Introduction to Machine Learning
[http://www.e-booksdirectory.com/details.php?ebook=4493](http://www.e-booksdirectory.com/details.php?ebook=4493)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
[http://www.e-booksdirectory.com/details.php?ebook=3267](http://www.e-booksdirectory.com/details.php?ebook=3267)

Reinforcement Learning by C. Weber, M. Elshaw, N. M. Mayer
[http://www.e-booksdirectory.com/details.php?ebook=3227](http://www.e-booksdirectory.com/details.php?ebook=3227)

Machine Learning by Abdelhamid Mellouk, Abdennacer Chebira
[http://www.e-booksdirectory.com/details.php?ebook=2852](http://www.e-booksdirectory.com/details.php?ebook=2852)

How Are We To Know? by Nils J. Nilsson
[http://www.e-booksdirectory.com/details.php?ebook=2710](http://www.e-booksdirectory.com/details.php?ebook=2710)

Reinforcement Learning: An Introduction
[http://www.e-booksdirectory.com/details.php?ebook=1825](http://www.e-booksdirectory.com/details.php?ebook=1825)

Gaussian Processes for Machine Learning
[http://www.e-booksdirectory.com/details.php?ebook=1774](http://www.e-booksdirectory.com/details.php?ebook=1774)

Machine Learning, Neural and Statistical Classification
[http://www.e-booksdirectory.com/details.php?ebook=1118](http://www.e-booksdirectory.com/details.php?ebook=1118)

Introduction To Machine Learning
[http://www.e-booksdirectory.com/details.php?ebook=1117](http://www.e-booksdirectory.com/details.php?ebook=1117)

Inductive Logic Programming: Techniques and Applications
[http://www.e-booksdirectory.com/details.php?ebook=1105](http://www.e-booksdirectory.com/details.php?ebook=1105)

Practical Artificial Intelligence Programming in Java
[http://www.e-booksdirectory.com/details.php?ebook=32](http://www.e-booksdirectory.com/details.php?ebook=32)

Information Theory, Inference, and Learning Algorithms
[http://www.e-booksdirectory.com/details.php?ebook=21](http://www.e-booksdirectory.com/details.php?ebook=21)

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hengheng
The bad part about eBooks is that they always pile up. They are probably the
most non-read books in existence. Or why should I bother reading 16 eBooks on
the same topic, when reading a single good one would be the sane solution (the
one I'd choose for paper books)?

~~~
farresito
I certainly agree with you. Often, it's just better to buy a paper book than
try to read a little bit here and there. After all, paper books are not that
expensive. My personal problem is than I often buy books that I never end up
reading, or that I read after years (five, six, or even more). I'm pretty sure
I'm not the only one that suffers from this, though.

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hbbio
Interesting.

I didn't knew that the Hastie/Tibshirani/Friedman was legally available as a
free download. I would recommend it to anyone with a sufficient maths/stats
background.

~~~
tom_b
Yes, both 'The Elements of Statistical Learning' and 'An Introduction to
Statistical Learning with Applications in R' are available free in pdf.

For fans of hard copy, I recently found that if your local (university?)
library is a SpringerLink customer, you can purchase a print-on-demand copy of
either book for $26.99, which includes shipping. Interior pages are in black
and white (including the graphs), but that is a really cheap price for these
two.

Andrew Ng's course notes from his physical class at Stanford (CS 229 - Machine
Learning) are extensive and available as well at:

[http://cs229.stanford.edu/materials.html](http://cs229.stanford.edu/materials.html)

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nocoment
Dangerous links (Google ad network masquerading as downloads of the content.)

First listing is from a commercial solver, rather sales oriented, though it
looks like the topics may not depend on it?

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etherealG
i'd suggest following prof ng's free online course instead, just finished it a
few weeks ago and it's really good!

[https://class.coursera.org/ml-004/](https://class.coursera.org/ml-004/)

~~~
11001
I'd suggest going back to the original youtube[1] and course materials[2].
Coursera version is nothing but a hand-wavy watered down "feel good" version
of the original class. I also really like the Caltech's take "Learning from
data"[3]

[1]
[https://www.youtube.com/watch?v=UzxYlbK2c7E](https://www.youtube.com/watch?v=UzxYlbK2c7E)

[2] [http://cs229.stanford.edu/](http://cs229.stanford.edu/)

[3]
[http://work.caltech.edu/telecourse.html](http://work.caltech.edu/telecourse.html)

~~~
etherealG
sorry but i disagree. the in video questions, the randomised problem sets, all
the coursera stuff really helped me to learn the ideas.

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kurumo
While not free, 'Machine Learning: A Probabilistic Perspective'
([http://www.amazon.co.uk/gp/aw/d/0262018020](http://www.amazon.co.uk/gp/aw/d/0262018020))
is the best book I have found so far. I also second the recommendations for
Tibshirani's and MacKay's books; the former for mathematical foundations, the
latter for the intuition.

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alleycat
[http://www.amazon.com/Programming-Collective-Intelligence-
Bu...](http://www.amazon.com/Programming-Collective-Intelligence-Building-
Applications/dp/0596529325)

I found this to be quite a good introduction.

~~~
gautamnarula
I also have this book and highly recommend it.

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nichochar
First of all thanks for sharing. I would like to study machine learning. I
have a good code and math background. Which of these books is the most
recommended?

~~~
snotrockets
"The Elements of Statistical Learning" is great. It assumes an astute reader,
but if you've made it through some post-graduate level work, you'd be fine.

