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Artificial Intelligence, Big Data and Data Science in Statistics

Bag om Artificial Intelligence, Big Data and Data Science in Statistics

This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book¿s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and datascientists, as well as engineers and computer scientists working in related fields or applications.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031071577
  • Indbinding:
  • Paperback
  • Sideantal:
  • 384
  • Udgivet:
  • 16. November 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 155x21x235 mm.
  • Vægt:
  • 581 g.
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Leveringstid: 2-3 uger
Forventet levering: 18. Oktober 2024

Beskrivelse af Artificial Intelligence, Big Data and Data Science in Statistics

This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book¿s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and datascientists, as well as engineers and computer scientists working in related fields or applications.

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