TY  - BOOK
AU  - Murphy,Kevin P.
TI  - Machine learning: a probabilistic perspective 
T2  - Adaptive computation and machine learning
SN  - 9780262018029 (hardcover : alk. paper)
U1  - 006.31 22
PY  - 2012///
CY  - Cambridge, Massachusetts
PB  - Massachusetts Institute of Technology (The MIT Press)
KW  - Machine learning
KW  - BUEsh
KW  - Probabilities
KW  - COMSCI
KW  - August2015
KW  - December2015
KW  - January2016
N1  - Index : p.1051-1071; Bibliography : p. 1019- 1050
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  -