MACHINE LEARNING: A PROBABISTIC PERSPECTIVE (Record no. 592726)

MARC details
000 -LEADER
fixed length control field 02005nam a22001457a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 171108b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 978-0-262-01802-9
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/MUR
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name MURPHY KEVIN
245 ## - TITLE STATEMENT
Title MACHINE LEARNING: A PROBABISTIC PERSPECTIVE
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc AMERICA
Name of publisher, distributor, etc UNITED STATES OF AMERICA
Date of publication, distribution, etc 2012
300 ## - PHYSICAL DESCRIPTION
Extent xxix, 1071
520 ## - SUMMARY, ETC.
Summary, etc This book 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 Ref.Book offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.<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.<br/>Author is a research scientist at google. Previously, he was Asst. Professor of computer science and statistics at the University of british Columbia.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Full call number Barcode Date last seen Koha item type Public note
    Dewey Decimal Classification     Reference Symbiosis Institute of Computer Studies and Research Symbiosis Institute of Computer Studies and Research Reference 08/11/2017 TECHNICAL BOOK SERVICES 6951.00 006.3/MUR SICSR-B-19427 29/03/2018 Books MACHINE LEARNING, PROBABILITIES.