Free Online Book: Bayesian Reasoning and Machine Learning

I found it helpful to read through Think Stats and Think Bayes before tackling a machine learning book.<p>[1] Think Stats: <a href="http://www.greenteapress.com/thinkstats/thinkstats.pdf" rel="nofollow">http://www.greenteapress.com/thinkstats/thinkstats.pdf</a><p>[2] Think Bayes: <a href="http://www.greenteapress.com/thinkbayes/thinkbayes.pdf" rel="nofollow">http://www.greenteapress.com/thinkbayes/thinkbayes.pdf</a>

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).

The first few chapters of<p>ET Jaynes: 'Probability Theory: The Logic of Science': <a href="http://bayes.wustl.edu/etj/prob/book.pdf" rel="nofollow">http://bayes.wustl.edu/etj/prob/book.pdf</a><p>Are great (and free) as a thorough introduction to bayesian reasoning.

Actual book is here (warning 13 MB pdf):<p><a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online" rel="nofollow">http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...</a><p>Was delighted to see a notation list as the second page in the book.

MacKay's Information Theory, Inference and Learning Algorithms: <a href="http://www.inference.phy.cam.ac.uk/mackay/itila/" rel="nofollow">http://www.inference.phy.cam.ac.uk/mackay/itila/</a><p>Elementals of Statistical Learning: <a href="http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html" rel="nofollow">http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html</a>

The best advice i can give is to go through this video, it was fun and really helped me a lot. <a href="http://pyvideo.org/video/608/bayesian-statistics-made-as-simple-as-possible" rel="nofollow">http://pyvideo.org/video/608/bayesian-statistics-made-as-sim...</a>

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.

[For the really lazy]<p>PDF download link:<p><a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf" rel="nofollow">http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf</a>