
Free Online Book: Bayesian Reasoning and Machine Learning - EzGraphs
http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage?from=Main.Textbook
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Bostwick
I found it helpful to read through Think Stats and Think Bayes before tackling
a machine learning book.

[1] Think Stats: <http://www.greenteapress.com/thinkstats/thinkstats.pdf>

[2] Think Bayes: <http://www.greenteapress.com/thinkbayes/thinkbayes.pdf>

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Pwnguinz
As someone who has zero calc training nor linear algebra (some discrete
mathematics was all I took in University), what are some recommended start
point to most quickly be up to speed to digest the resources posted both in
the OP and by other commenters in this thread? Just a bit of background about
where I am at math-wise: I tried taking Andrew Ng's ML course, and quickly
fell behind starting with the second programming assignment (it was
implementing a linear regression algo, I believe).

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sampo
Andrew Ng's Machine Learning course at Coursera, week 1 contains about 1 hour
Linear Algebra review: lectures on vectors, matrices, their multiplication,
transpose and inverse.

So do you think these lectures are not enough to bring one up to speed in
applying these concepts in linear regression?

Of course, a formally educated person has taken a full semester of Linear
Algebra, and solved dozens of homeworks of "transpose this", "invert that"
etc. so it's difficult to guess how much homework of the boring kind would be
needed before one is able to apply these concepts in problem solving.

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ulvund
The first few chapters of

ET Jaynes: 'Probability Theory: The Logic of Science':
<http://bayes.wustl.edu/etj/prob/book.pdf>

Are great (and free) as a thorough introduction to bayesian reasoning.

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EzGraphs
Actual book is here (warning 13 MB pdf):

[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=Brml.Online)

Was delighted to see a notation list as the second page in the book.

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bhickey
MacKay's Information Theory, Inference and Learning Algorithms:
<http://www.inference.phy.cam.ac.uk/mackay/itila/>

Elementals of Statistical Learning: <http://www-
stat.stanford.edu/~tibs/ElemStatLearn/index.html>

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nashequilibrium
The best advice i can give is to go through this video, it was fun and really
helped me a lot. [http://pyvideo.org/video/608/bayesian-statistics-made-as-
sim...](http://pyvideo.org/video/608/bayesian-statistics-made-as-simple-as-
possible)

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brianobush
Are there books on practical machine learning? The math is fine in these
books, but does not address the practical side: data analysis, pre-processing,
on-line pattern recognition, etc.

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basman
Not a book, but Andrew Ng's coursera course (or Stanford class video lectures)
are great and have lots of practical tips.

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Toshio
[For the really lazy]

PDF download link:

<http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf>

