
Information Theory, Inference, and Learning Algorithms (2003) [pdf] - Tomte
http://www.inference.phy.cam.ac.uk/itprnn/book.pdf
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power
There are some great videos by the author on the same subject at:
[http://videolectures.net/course_information_theory_pattern_r...](http://videolectures.net/course_information_theory_pattern_recognition/)
They cover a lot of the same ground but in a gentler way so they're good for
building intuition before working through the book fully.

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aussie_dev
These look great, thanks.

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imurray
David MacKay's landing page:
[http://www.inference.phy.cam.ac.uk/mackay/itila/](http://www.inference.phy.cam.ac.uk/mackay/itila/)

Other formats are available along with some other information.

David's course and drafts of this book were my introduction to machine
learning. It starts out very accessible (for those with maths at undergraduate
science-subject level or a very good high-school), and also contains more
dense advanced material later on. This is a book with hidden gems throughout
that you can return to many times.

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oneisone
I read a chapter of the book, it criticizes non Bayesian statistics too much.
For example at discussing p-values. The p-value method is for getting sound
results but not for interpreting the particular value obtained in one
experiment, for example 5% is for being right 19 of 20 times, the numbers
obtained in the experiments doesn't change this.

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CuriouslyC
The author is trying to present a probabalistic framework for reasoning about
the world. The issue with p-values is that they have a narrow band of utility,
and can't really contribute much to the process of inference as a whole; you
can say no to a nearly infinite number of absurd propositions and still not be
able to say anything insightful about a system. On top of that, p-values are
clearly not intuitive, as demonstrated by their widespread misuse.

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signa11
honestly, i tried reading through the initial chapters, and found them too be
waay too much for me. any pointers to gentler introduction to the subject
matter ? thank you !

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agumonkey
Only 24 hours in a day, and so much to learn about.

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signa11
> Only 24 hours in a day, and so much to learn about.

why waste time learning when ignorance is instantaneous ?

