Introduction to Environmental Data Science. (Record no. 60497)

MARC details
000 -LEADER
fixed length control field 03917cam a2200481 i 4500
001 - CONTROL NUMBER
control field on1355502288
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240904140220.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221214t20232023nyua b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2022054278
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781107065550
Qualifying information (hardback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1107065550
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1355502288
040 ## - CATALOGING SOURCE
Original cataloging agency DLC
Language of cataloging eng
Description conventions rda
Transcribing agency DLC
Modifying agency OCLCF
-- YDX
-- OCLCO
-- Y@Y
-- CNR
042 ## - AUTHENTICATION CODE
Authentication code pcc
049 ## - LOCAL HOLDINGS (OCLC)
Holding library CNRM
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number GE45.D37
Item number H74 2023
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 363.700285
Edition number 23/eng20221219
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Hsieh, William Wei,
Dates associated with a name 1955-
Relator term author
Affiliation University of British Columbia
245 10 - TITLE STATEMENT
Title Introduction to Environmental Data Science.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cambridge ;
-- New York :
Name of producer, publisher, distributor, manufacturer Cambridge University Press,
Date of production, publication, distribution, manufacture, or copyright notice 2023.
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice 2023.
300 ## - PHYSICAL DESCRIPTION
Extent xx, 627 pages :
Other physical details illustrations ;
Dimensions 25 cm
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term unmediated
Media type code n
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Carrier type code nc
Source rdacarrier
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Basics -- Probability distributions -- Statistical inference -- Linear regression -- Neural networks -- Non-linear optimization -- Learning and generalization -- Principal components and canonical correlation -- Unsupervised learning -- Time series -- Classification -- Kernel methods -- Decision trees, random forests and boosting -- Deep learning -- Forecast verification and post-processing -- Merging of machine learning and physics.
520 ## - SUMMARY, ETC.
Summary, etc. "Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management of agriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data. William W. Hsieh is a professor emeritus in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia. Known as a pioneer in introducing machine learning to environmental science, he has written over 100 peer-reviewed journal papers on climate variability, machine learning, atmospheric science, oceanography, hydrology and agricultural science. He is the author of the book Machine Learning Methods in the Environmental Sciences (2009, Cambridge University Press), the first single-authored textbook on machine learning for environmental scientists. Currently retired in Victoria, British Columbia, he enjoys growing organic vegetables"--
Assigning source Provided by publisher.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environmental sciences
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environmental protection
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environmental management
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Sciences de l'environnement
General subdivision Informatique.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environnement
General subdivision Protection
-- Informatique.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environnement
General subdivision Gestion
-- Informatique.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Apprentissage automatique.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environmental management
General subdivision Data processing
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environmental protection
General subdivision Data processing
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Environmental sciences
General subdivision Data processing
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
Source of heading or term fast
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type BOOK
Source of classification or shelving scheme Library of Congress Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Library of Congress Classification     NCAR Library NCAR Library Foothills Lab 09/04/2024   GE45 .D37 .H74 2023 50583020031799 09/04/2024 1 09/04/2024 BOOK

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