rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial Intelligence, Big Data and Data Science in Statistics
    sub title: Challenges and Solutions in Environmetrics, the Natural Sciences and Technology
  name:
    name part: Steland, Ansgar.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Tsui, Kwok-Leung.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (VIII, 376 pages 1 illustrations)
  abstract: 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 data scientists, as well as engineers and computer scientists working in related fields or applications.
  note: edited by Ansgar Steland, Kwok-Leung Tsui.
  subject:
    topic: Statistics
  subject:
    topic: Artificial intelligence-Data processing
  subject:
    topic: Machine learning
  subject:
    topic: Big data
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Data Science
  subject:
    topic: Machine Learning
  subject:
    topic: Big Data
  subject:
    topic: Applied Statistics
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9978-3-031-07155-3
  identifier: 9783031071553
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 221115
    record change date: 20240805182314.6
    record identifier: in00004659452