4 ms·

Free Online Book: Bayesian Reasoning and Machine Learning

134 points by EzGraphs 14y ago

EzGraphs 14y ago
Actual book is here (warning 13 MB pdf): http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... Was delighted to see a notation list as the second page in the book.
Toshio 14y ago
[For the really lazy] PDF download link: http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf
Bostwick 14y ago
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 http://www.greenteapress.com/thinkstats/thinkstats.pdf [2] Think Bayes: http://www.greenteapress.com/thinkbayes/thinkbayes.pdf http://www.greenteapress.com/thinkbayes/thinkbayes.pdf
ulvund 14y ago
The first few chapters of ET Jaynes: 'Probability Theory: The Logic of Science': http://bayes.wustl.edu/etj/prob/book.pdf http://bayes.wustl.edu/etj/prob/book.pdf Are great (and free) as a thorough introduction to bayesian reasoning.
brianobush 14y ago
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.
- basman 14y ago
  Not a book, but Andrew Ng's coursera course (or Stanford class video lectures) are great and have lots of practical tips.
- 0ren 14y ago
  Try Segaran's Programming Collective Intelligence.
Pwnguinz 14y ago
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).
- sampo 14y ago
  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.
bhickey 14y ago
MacKay's Information Theory, Inference and Learning Algorithms: http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/ Elementals of Statistical Learning: http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html
nashequilibrium 14y ago
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-simple-as-possible http://pyvideo.org/video/608/bayesian-statistics-made-as-sim...

134 points by EzGraphs 14y ago

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

Toshio 14y ago

[For the really lazy] PDF download link: http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/270212.pdf

Bostwick 14y ago

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

ulvund 14y ago

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

brianobush 14y ago

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.

basman 14y ago
Not a book, but Andrew Ng's coursera course (or Stanford class video lectures) are great and have lots of practical tips.
0ren 14y ago
Try Segaran's Programming Collective Intelligence.

Pwnguinz 14y ago

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).

sampo 14y ago
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.

bhickey 14y ago

MacKay's Information Theory, Inference and Learning Algorithms: http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/ Elementals of Statistical Learning: http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html

nashequilibrium 14y ago

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-simple-as-possible http://pyvideo.org/video/608/bayesian-statistics-made-as-sim...