Machine Learning: A Probabilistic Perspective

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MIT Press, Aug 24, 2012 - Computers - 1067 pages

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

 

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Contents

Probability
27
Generative models for discrete data
67
471
90
Gaussian models
99
Bayesian statistics
151
5
164
1
181
111
182
Exact inference for graphical models
709
Variational inference
733
More variational inference
769
Monte Carlo inference
817
Markov chain Monte Carlo MCMC inference
839
Clustering
877
Graphical model structure learning
909
Latent variable models for discrete data
949

129
190
Frequentist statistics
193
6
213
Linear regression
219
Logistic regression
247
Generalized linear models and the exponential family
283
Directed graphical models Bayes nets
309
Mixture models and the EM algorithm
339
Latent linear models
383
Sparse linear models
423
202
479
Kernels
481
Gaussian processes
517
Adaptive basis function models
545
Markov and hidden Markov models
591
State space models
633
Undirected graphical models Markov random fields
663
649
996
Deep learning
999
Notation
1013
Bibliography
1019
602
1027
Indexes
1051
348
1052
663
1055
724
1056
881
1057
298
1059
437
1060
547
1061
983
1065
500
1067
928
1069
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About the author (2012)

Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.

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