Machine Learning: A Probabilistic Perspective

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

1 Introduction
1
2 Probability
27
3 Generative Models for Discrete Data
65
4 Gaussian Models
97
5 Bayesian Statistics
149
6 Frequentist Statistics
191
7 Linear Regression
217
8 Logistic Regression
245
18 State Space Models
631
19 Undirected Graphical Models Markov Random Fields
661
20 Exact Inference for Graphical Models
707
21 Variational Inference
731
22 More Variational Inference
767
23 Monte Carlo Inference
815
24 Markov Chain Monte Carlo MCMC Inference
837
25 Clustering
875

9 Generalized Linear Models and the Exponential Family
281
10 Directed Graphical Models Bayes Nets
307
11 Mixture Models and the EM Algorithm
337
12 Latent Linear Models
381
13 Sparse Linear Models
421
14 Kernels
479
15 Gaussian Processes
515
16 Adaptive Basis Function Models
543
17 Markov and Hidden Markov Models
589
26 Graphical Model Structure Learning
907
27 Latent Variable Models for Discrete Data
945
28 Deep Learning
995
Notation
1009
Bibliography
1015
Index to Code
1047
Index to Keywords
1050
Copyright

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About the author (2012)

Kevin P. Murphy is a Research Scientist at Google. Previously, he was Associate Professor of Computer Science and Statistics at the University of British Columbia.

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