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Machine Learning:The art and science of algorithm that make sense of Data (Record no. 2485)

MARC details
000 -LEADER
fixed length control field 02275nam a22002057a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200224120458.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200118b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 978-1-316-50611-0
028 ## - PUBLISHER NUMBER
Source Allied Informatics, Jaipur
Bill Number 7084
Bill Date 13/01/2020
Purchase Year 2019-20
040 ## - CATALOGING SOURCE
Original cataloging agency BSDU
Language of cataloging English
Transcribing agency BSDU
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number FLA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Flach,Peter
245 ## - TITLE STATEMENT
Title Machine Learning:The art and science of algorithm that make sense of Data
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New Delhi
Name of publisher, distributor, etc. Cambridge University Press
Date of publication, distribution, etc. 2019
300 ## - PHYSICAL DESCRIPTION
Extent 396
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note <br/>As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.<br/><br/>Prologue and Chapter 1 are freely available online<br/>Pedagogic features include boxes summarising relevant background material and a list of 'important points to remember'<br/>Epilogue includes open problems in machine learning<br/><br/>Contents<br/><br/>Prologue: a machine learning sampler<br/>1. The ingredients of machine learning<br/>2. Binary classification and related tasks<br/>3. Beyond binary classification<br/>4. Concept learning<br/>5. Tree models<br/>6. Rule models<br/>7. Linear models<br/>8. Distance-based models<br/>9. Probabilistic models<br/>10. Features<br/>11. In brief: model ensembles<br/>12. In brief: machine learning experiments<br/>Epilogue: where to go from here<br/>Important points to remember<br/>Bibliography<br/>Index.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha 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 Date acquired Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification   Not For Loan Reference BSDU Knowledge Resource Center, Jaipur BSDU Knowledge Resource Center, Jaipur 01/18/2020 1595.00   006.31 FLA 018040 02/12/2020 1595.00 01/18/2020 Books