Gaussian Processes for Machine Learning
by Carl E. Rasmussen, Christopher K. I. Williams
Publisher: The MIT Press 2005
Number of pages: 266
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. 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.
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by Dimitri P. Bertsekas - Athena Scientific
The book considers large and challenging multistage decision problems, which can be solved by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations.
by Alex Smola, S.V.N. Vishwanathan - Cambridge University Press
Over the past two decades Machine Learning has become one of the mainstays of information technology and a rather central part of our life. Smart data analysis will become even more pervasive as a necessary ingredient for technological progress.
by Osvaldo Simeone - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
by M. Mohri, A. Rostamizadeh, A. Talwalkar - The MIT Press
This is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools.