Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)

Even though this is not a cookbook on Gaussian Processes, the explanations are clear and to the point.The book is highly technical but it also does a great job explaining how Gaussian Processes fit in the big picture regarding the last few decades in the Machine Learning field and how they are related in some ways to both SVM and Neural Networks.I'm still working my way through the book but so far I'm extremely pleased with it. As the first reviewer said, it's an evolving subject so keep looking for new material.It's a well-edited hardcover book and at this price it's a steal.

This is another great book on ML. Although title suggests that it is solely about GP, author manages to include a lot on general ML in such a small volume (but, yes it is mostly about GP). If you are already familiar with basics of ML, this book may help you understand some details. And, of course GP techniques produce really nice plots; even this fact alone is enough to try.

This is my reference book for my phd research on Gaussian process.

Good!

This is arguably the best intro to GP for ML that I have read to date. This book provides the necessary background to follow any particular line of literature in GP research. Must have.

A specific advantage of this book is that it is one of the few that dedicate a whole chapter on the connection between Bayesian methods using Gaussian Processes and Reproducing Kernel Hilbert Spaces. Even if this connection is a posteriori pretty obvious, it is nice to have it broken down clearly into small understandable pieces.Otherwise, all the explanations concerning Gaussian Processes themselves for regression and classification are very clear and make this book a very worthwhile read. I would recommend also reading other books focusing more on Reproducing Kernel Hilbert Spaces in order to have a complete picture of these methods (e.g. "Learning with Kernels" by Scholkopf and Smola or for an even broader picture "Generalized Additive Models" by Hastie and Tibshirani).Finally, since GP and RKHS for classification are still evolving subjects, it is probably a good idea to keep reading more material on them after finishing this book.

I did not enjoy reading this book and it did not encourage me to learn more about the topic.My main criticism is about one of the main claims of this book: It is not self-contained.For example, on p. 95, the authors start talking about random processes without defining what they mean.I come from a machine learning background and I find the language very silly. Explanations are dictionary-style with circulative explanations.Nevertheless, the content seems to be correct.

Arrived in great condition.

Good book.

Essential read. Excellent software support (MATLAB.) Thank you.

it looks very good!!!

O fornecedor me enviou um tracking number falso; o livro chegou na data limite da solicitação da garantia de A a Z. Eu comprei de outro fornecedor e agora estou com duas unidades em casa. Há vários relatos de golpes envolvendo a Ergodebooks. Não recomendo comprar com eles. Meu pedido chegou mas acho que foi pura sorte.