Machine Learning (Record no. 11917)

MARC details
000 -LEADER
fixed length control field 02256nmm a2200181Ia 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780262018029
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number M954M
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Murphy, Kevin P.
Relator term Author
Language of a work English
9 (RLIN) 745
245 #0 - TITLE STATEMENT
Title Machine Learning
Remainder of title : A Probabilistic Perspective
Statement of responsibility, etc. / by Kevin P. Murphy.
Medium [Electronic Resource]
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cambridge, Mass
Name of publisher, distributor, etc. : The MIT Press,
Date of publication, distribution, etc. 2012
300 ## - PHYSICAL DESCRIPTION
Extent 1071p.
520 ## - SUMMARY, ETC.
Summary, etc. A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
9 (RLIN) 15461
Topical term or geographic name entry element Probabilities
9 (RLIN) 15462
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=480968">http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=480968</a>
Electronic format type PDF
Link text Click to Access the Online Book
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type e-Book
Suppress in OPAC
Holdings
Withdrawn status Lost status Damaged status Use restrictions Not for loan Collection Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type Public note
      e-Book For Access   Textbook S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Online 2022-09-20 Infokart India Pvt. Ltd., New Delhi 165.00   006.31 M954M EB0053 2022-09-20 2022-09-20 e-Book Platform : EBSCO