TY  - BOOK
AU  - Murphy,Kevin P.
TI  - Machine learning: a probabilistic perspective 
T2  - Adaptive computation and machine learning series
SN  - 9780262018029 
AV  - Q325.5 .M87 2012
U1  - 006.31 MU.M 2012 23
PY  - 2012///
CY  - Cambridge, Mass. : 
PB  - MIT Press
KW  - Machine learning
KW  - Probabilities
N1  - Includes bibliographical references (pages 1015-1045) and indexes
N2  - "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"--Back cover
ER  -