Machine Learning: A Probabilistic Perspective (Record no. 39802)

MARC details
000 -LEADER
fixed length control field 01890 a2200169 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250120130321.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780262018029
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 MUR
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Murphy, Kevin P
245 ## - TITLE STATEMENT
Title Machine Learning: A Probabilistic Perspective
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc MIT Press
Date of publication, distribution, etc 2012
Place of publication, distribution, etc Cambridge, Landon
300 ## - PHYSICAL DESCRIPTION
Extent 1071p
520 ## - SUMMARY, ETC.
Summary, etc 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.<br/><br/>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.
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name Machine Learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Algorithms
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book

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