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www.stanford.edu
Book web site includes links to a full course, software, and other material....
Stephen Boyd, Lieven Vandenberghe
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4
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robotics.stanford.edu
This is an introductory book about machine learning. Notice that this is a draft book....
Nils J. Nilsson
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9
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www.gaussianprocess.org
"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....
Carl Edward Rasmussen and Christopher K. I. Williams
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7
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www.amsta.leeds.ac.uk
This book covers several topics such as Classification, Classical Statistical Methods, Modern Statistical Techniques, Machine Learning of Rules and Trees, Neural Networks
Methods for Comparison, Review of Previous Empirical Comparisons, Dataset Descriptions and Results, Analysis of Results, Knowledge Representation, and Learning to Control Dynamical Systems
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Ellis Horwood
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3
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markwatson.com
This book shows how to implement some AI techniques in java such as Search, Reasoning, Semantic Web, Expert Systems, Genetic Algorithms, Neural Networks, Machine Learning with Weka, Statistical Natural Language Processing, and Information Gathering....
Mark Watson
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4
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homepages.inf.ed.ac.uk
"Cluster analysis is an important technique in the rapidly growing field known as exploratory data analysis and is being applied in a variety of engineering and scientific disciplines such as biology, psyohology, medicine, marketing, computer vision, and remote sensing....
A. K. Jain, R. C. Dubes
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2
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www.inference.phy.cam.ac.uk
This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and linear algebra as taught in a 1st- or second-year undergraduate course on mathematics for scientists and engineers....
David J. C. MacKay
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3
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www.inf.fu-berlin.de
This book covers the following topics: The biological paradigm, Threshold logic, Weighted Networks, The Perceptron, Perceptron learning, Unsupervised learning and clustering algorithms, One and two layered networks, The back-propagation algorithm, Fast learning algorithms, Statistics and Neural Networks, The complexity of learning, Fuzzy Logic, Associative Networks, The Hopfield Model, Stochastic ...
Raul Rojas
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5
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