For those who want a more solid take on machine learning, and who still remember their math and probability/statistics, (i.e. advanced undergrad or new grad student), the best texts seem to be:
The Elements of Statistical Learning by Hastie, Tibshirani and Friedman, available for free on line.
Pattern Recognition and Machine Learning by Chris Bishop. Very Bayesian.
Machine Learning: A Probabilistic Perspective by Kevin Murphy. Also Bayesian, although not as Bayesian as Bishop. The most recent of the three, and therefore covers a few topics not covered elsewhere like deep learning and conditional random fields. The first few printings are full of errors and confusing passages, should be better before too long.
It differs from other books in that all the material is treated from the unified perspective of statistical learning theory and VC dimension, as a result the book feels less like a hodgepodge of unrelated techniques and more like an introduction to a coherent field.
Hastie and Tibshirani also have a new, less demanding mathematically book out:
Both require a willingness to immerse in the mathematics. But the maths isn't hard, even for someone like me who hasn't formally studied it since I was 15.
Comments
For those who want a more solid take on machine learning, and who still remember their math and probability/statistics, (i.e. advanced undergrad or new grad student), the best texts seem to be:
The Elements of Statistical Learning by Hastie, Tibshirani and Friedman, available for free on line.
Pattern Recognition and Machine Learning by Chris Bishop. Very Bayesian.
Machine Learning: A Probabilistic Perspective by Kevin Murphy. Also Bayesian, although not as Bayesian as Bishop. The most recent of the three, and therefore covers a few topics not covered elsewhere like deep learning and conditional random fields. The first few printings are full of errors and confusing passages, should be better before too long.
Did I miss any?
The best introductory book and also the most cohesive one is "Learning from Data" from Yaser Abu-Mostafa, accompanied by great video lectures:
http://amlbook.com/
It differs from other books in that all the material is treated from the unified perspective of statistical learning theory and VC dimension, as a result the book feels less like a hodgepodge of unrelated techniques and more like an introduction to a coherent field.
Hastie and Tibshirani also have a new, less demanding mathematically book out:
http://www.amazon.com/Introduction-Statistical-Learning-Appl...
I would add to that two classics with free PDFs available from the authors' websites:
1. David Barber: Bayesian Reasoning and Machine Learning
2. David MacKay: Information Theory, Inference, and Learning Algorithms
[1] http://www.cs.ucl.ac.uk/staff/d.barber/brml/
[2] http://www.inference.phy.cam.ac.uk/mackay/itila/
Both require a willingness to immerse in the mathematics. But the maths isn't hard, even for someone like me who hasn't formally studied it since I was 15.
There's quite a few good free ones of varying perspectives
http://metaoptimize.com/qa/questions/186/good-freely-availab...
Esp:
http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...
http://www.inference.phy.cam.ac.uk/itprnn/book.html
http://alex.smola.org/drafts/thebook.pdf
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and MLPP errata: http://www.cs.ubc.ca/~murphyk/MLbook/errata.html
Pg's own A Plan for Spam is also an excellent introduction to Bayes for boolean comparison.
http://www.paulgraham.com/spam.html