About the book
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Editions of Gaussian Processes for Machine Learning
- Hardcover
- ISBN 9780262182539
- EBook
- ISBN 9780262253321
- EBook
- ISBN 9780262256834
- EBook
- ISBN 9780262261074
Read an Excerpt
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Frequently Asked Questions
What is Gaussian Processes for Machine Learning about?
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
What core themes, tropes, or subjects are explored in Gaussian Processes for Machine Learning?
Computing and Information Technology > Computer science
Where can I read a sample of Gaussian Processes for Machine Learning?
You can read an official preview of the few pages here https://www.book2look.com/book/9780262182539
Is Gaussian Processes for Machine Learning part of a series, and can it be read as a standalone?
Yes it is a part of series Adaptive Computation and Machine Learning series
Who is/are the Author/s of the book Gaussian Processes for Machine Learning?
Carl Edward Rasmussen, Christopher K. I. Williams
Who is the Publisher of the book Gaussian Processes for Machine Learning?
MIT Press
What are the ISBN numbers for the physical and digital editions?
Gaussian Processes for Machine Learning is available as hardcover(ISBN 9780262182539) and ebook(ISBN 9780262253321) and ebook(ISBN 9780262256834) and ebook(ISBN 9780262261074)
Where can I buy Gaussian Processes for Machine Learning online or support local independent bookshops?
You can buy Gaussian Processes for Machine Learning from below
Where can I buy Gaussian Processes for Machine Learning?