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May I also recommend a related book, also a free PDF: http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/ "Inform

May I also recommend a related book, also a free PDF: http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/ "Information theory, inference, and learning algorithms" by David Mackay. This book is not a pure machine learning book but it is a very fun read.

tarkin2 17y ago
http://www.inference.phy.cam.ac.uk/mackay/itila/Potter.html http://www.inference.phy.cam.ac.uk/mackay/itila/Potter.html I haven't read it, but this comparison has convinced me of its worth.
jimbokun 17y ago
Anyone want to compare and contrast the two? Is one better overall? Do the books have different strengths and weaknesses?
- catzaa 17y ago
  The McKay textbook is more about Bayesian inference and a few neural network chapters. Hastie is more of a statistical approach with statistical rigor.
- arohner 17y ago
  I'm mainly interested in Machine Learning algorithms. I've read the first 75 pages of this book, and skimmed most of Mackay, and I prefer this one. It goes into a lot more detail on the performance of different algorithms, how the handle sparse data, error rates and high dimensionality. McKay's looks like a good book, but appears more applicable to pure information theory rather than ML specifically.
- pz 17y ago
  A lot of the Mackay book is on information/coding theory and while it will deepen an existing understanding of ML, its probably a roundabout introduction. ESL is a much better intro, especially for someone looking to apply ML. That said, it is meant for people who are comfortable with math/stats; its much more statistics oriented than, say, Mitchell's book. But they do a good job of explaining things in non-math language. This book does a good job of exposing high level ML concepts (e.g. bias-variance tradeoff) but still teaches a lot of the standard methods & tools. These are actually two of my favorite books on the subject and I can't recommend them both enough.

tarkin2 17y ago

http://www.inference.phy.cam.ac.uk/mackay/itila/Potter.html http://www.inference.phy.cam.ac.uk/mackay/itila/Potter.html I haven't read it, but this comparison has convinced me of its worth.

jimbokun 17y ago

Anyone want to compare and contrast the two? Is one better overall? Do the books have different strengths and weaknesses?

catzaa 17y ago
The McKay textbook is more about Bayesian inference and a few neural network chapters. Hastie is more of a statistical approach with statistical rigor.
arohner 17y ago
I'm mainly interested in Machine Learning algorithms. I've read the first 75 pages of this book, and skimmed most of Mackay, and I prefer this one. It goes into a lot more detail on the performance of different algorithms, how the handle sparse data, error rates and high dimensionality. McKay's looks like a good book, but appears more applicable to pure information theory rather than ML specifically.
pz 17y ago
A lot of the Mackay book is on information/coding theory and while it will deepen an existing understanding of ML, its probably a roundabout introduction. ESL is a much better intro, especially for someone looking to apply ML. That said, it is meant for people who are comfortable with math/stats; its much more statistics oriented than, say, Mitchell's book. But they do a good job of explaining things in non-math language. This book does a good job of exposing high level ML concepts (e.g. bias-variance tradeoff) but still teaches a lot of the standard methods & tools. These are actually two of my favorite books on the subject and I can't recommend them both enough.