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Machine Learning: A Probabilistic Perspective - Murphy

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 							Machine Learning: A Probabilistic Perspective
  
  Author: Kevin P. Murphy
 
ISBN: 978-0262018029
  File format: PDF
  
  
  
  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.
  
  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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