國立虎尾科技大學 |

Artificial Intelligence, Big Data and Data Science in Statistics = Challenges and Solutions in Environmetrics, the Natural Sciences and Technology /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial Intelligence, Big Data and Data Science in Statistics/ edited by Ansgar Steland, Kwok-Leung Tsui.
其他題名:	Challenges and Solutions in Environmetrics, the Natural Sciences and Technology /
其他作者:	Tsui, Kwok-Leung.
面頁冊數:	VIII, 376 p. 1 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Applied Statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-07155-3
ISBN:	9783031071553

Artificial Intelligence, Big Data and Data Science in Statistics = Challenges and Solutions in Environmetrics, the Natural Sciences and Technology /
Artificial Intelligence, Big Data and Data Science in StatisticsChallenges and Solutions in Environmetrics, the Natural Sciences and Technology /[electronic resource] :edited by Ansgar Steland, Kwok-Leung Tsui. - 1st ed. 2022. - VIII, 376 p. 1 illus.online resource.

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.

ISBN: 9783031071553

Standard No.: 10.1007/978-3-031-07155-3doiSubjects--Topical Terms:

1205141
Applied Statistics.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Artificial Intelligence, Big Data and Data Science in Statistics = Challenges and Solutions in Environmetrics, the Natural Sciences and Technology /
LDR:02542nam a22003855i 4500 001 1085451
003 DE-He213
005 20221115005420.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031071553 $9 978-3-031-07155-3
024 7 $a 10.1007/978-3-031-07155-3 $2 doi
035 $a 978-3-031-07155-3
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
245 1 0 $a Artificial Intelligence, Big Data and Data Science in Statistics $h [electronic resource] : $b Challenges and Solutions in Environmetrics, the Natural Sciences and Technology / $c edited by Ansgar Steland, Kwok-Leung Tsui.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a VIII, 376 p. 1 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
520 $a 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.
650 2 4 $a Applied Statistics. $3 1205141
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Data Science. $3 1174436
650 2 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1366002
650 1 4 $a Statistical Theory and Methods. $3 671396
650 0 $a Big data. $3 981821
650 0 $a Machine learning. $3 561253
650 0 $a Artificial intelligence—Data processing. $3 1366684
650 0 $a Statistics . $3 1253516
700 1 $a Tsui, Kwok-Leung. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1391916
700 1 $a Steland, Ansgar. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1066700
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031071546
776 0 8 $i Printed edition: $z 9783031071560
776 0 8 $i Printed edition: $z 9783031071577
856 4 0 $u https://doi.org/10.1007/978-3-031-07155-3
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)