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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 stu
by martincmartin 13y ago
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?
- gtani 13y agoThere's quite a few good free ones of varying perspectives http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning http://metaoptimize.com/qa/questions/186/good-freely-availab... Esp: http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... http://www.inference.phy.cam.ac.uk/itprnn/book.html http://www.inference.phy.cam.ac.uk/itprnn/book.html http://alex.smola.org/drafts/thebook.pdf http://alex.smola.org/drafts/thebook.pdf ___________ and MLPP errata: http://www.cs.ubc.ca/~murphyk/MLbook/errata.html http://www.cs.ubc.ca/~murphyk/MLbook/errata.html
- Blahah 13y agoI 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/ http://www.cs.ucl.ac.uk/staff/d.barber/brml/ [2] http://www.inference.phy.cam.ac.uk/mackay/itila/ 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.
- stiff 13y agoThe 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/ 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-Applications-Statistics/dp/1461471370/ref=sr_1_1?s=books&ie=UTF8&qid=1378737829&sr=1-1 http://www.amazon.com/Introduction-Statistical-Learning-Appl...
- baudehlo 13y agoPg's own A Plan for Spam is also an excellent introduction to Bayes for boolean comparison. http://www.paulgraham.com/spam.html http://www.paulgraham.com/spam.html