Gaussian processes for machine learning
by Carl Edward Rasmussen · first published 2005
83% recommend17% neutral
Why HN recommends it
Standard free textbook on Gaussian processes, cited as the go-to reference for the equations and theory behind GP regression and classification. Suits readers who want the mathematical foundations.
Summarised by Claude from recommending comments
Gaussian processes (GPs) provide an approach to kernel-machine learning. This book provides a 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. (From the book's web site, http://www.gaussianprocess.org/gpml/ )
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…also sklearn.gaussian_process [1] which offers Gaussian process classification and regression, Bayesian learning algorithms which can be thought of as analogs of the support vector machine. Rasmussen’s Gaussian Processes for Machine Learning (2006) is a great introduction despite being a little old. [2]. [0] https://scikit-learn.org/stable/modules/mixture.html [1] https://scikit-learn.org/stable/modules/gaussian_process.htm... [2] http://gaussianprocess.org/gpml/chapters/
on “Bayesian statistics and machine learning: How do they differ?” ↗…spikes that came with computing the kernel and mean predictions. It was a two-fold process. For understanding the exact equations, I would highly recommend the resource mentioned in the article [2], Gaussian Processes for Machine Learning Chapter 2. For a lecture video, I would recommend [3] which is from a ML course at Cornell. As for implementing GPs efficiently, this is still a challenge: numpy has a bunch of matrix-vector functions…
on “Gaussian Processes, not quite for dummies” ↗Have you seen Gaussian Processes for Machine Learning [0]? The entire text is freely available online at the mentioned URL. [0] http://www.gaussianprocess.org/gpml/
on “Machine Learning Exercises in Python, Part 1” ↗…Chebira http://www.e-booksdirectory.com/details.php?ebook=2852 How Are We To Know? by Nils J. Nilsson http://www.e-booksdirectory.com/details.php?ebook=2710 Reinforcement Learning: An Introduction http://www.e-booksdirectory.com/details.php?ebook=1825 Gaussian Processes for Machine Learning http://www.e-booksdirectory.com/details.php?ebook=1774 Machine Learning, Neural and Statistical Classification http://www.e-booksdirectory.com/details.php?ebook=1118 Introduction To Machine Learning http://www.e-booksdirectory.com/details.php?ebook=1117…
on “Free eBooks On Machine Learning” ↗You may be interested in the book Gaussian Processes for Machine Learning: http://gaussianprocess.org/gpml/ (freely available)
on “When Gradient Descent Is a Kernel Method” ↗…important difference between free & cheap. Easier access has all sorts of catalyzing effects. For example, a free textbook can be referred to. "For a more detailed discussion of this read chapter 4 of Gaussian Processes for Machine Learning."
on “Free Textbooks on Machine Learning” ↗