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Introduction to Environmental Data Science.

By: Publisher: Cambridge ; New York : Cambridge University Press, 2023Copyright date: 2023Description: xx, 627 pages : illustrations ; 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781107065550
  • 1107065550
Subject(s): DDC classification:
  • 363.700285 23/eng20221219
LOC classification:
  • GE45.D37 H74 2023
Contents:
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.
Summary: "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"-- Provided by publisher.
List(s) this item appears in: 2024 New Titles
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
BOOK BOOK NCAR Library Foothills Lab GE45 .D37 .H74 2023 1 Available 50583020031799
Total holds: 0

Includes bibliographical references and index.

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.

"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"-- Provided by publisher.

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