國立高雄大學圖資館 |

Data sciencean introduction to statistics and machine learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data scienceby Matthias Plaue.
其他題名:	an introduction to statistics and machine learning /
作者:	Plaue, Matthias.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg :2023.
面頁冊數:	xxiv, 361 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics.
電子資源:	https://doi.org/10.1007/978-3-662-67882-4
ISBN:	9783662678824$q(electronic bk.)

Data sciencean introduction to statistics and machine learning /
Plaue, Matthias.

Data sciencean introduction to statistics and machine learning /[electronic resource] :by Matthias Plaue. - Berlin, Heidelberg :Springer Berlin Heidelberg :2023. - xxiv, 361 p. :ill., digital ;24 cm.

Preface -- Part I Basics -- 1 Elements of data organization -- 2 Descriptive statistics -- Part II Stochastics -- 3 Probability theory -- 4 Inferential statistics -- 5 Multivariate statistics -- Part III Machine learning -- 6 Supervised machine learning -- 7 Unsupervised machine learning -- 8 Applications of machine learning -- Appendix -- A Exercises with answers -- B Mathematical preliminaries -- Supplementary literature -- Index.

Data science is the discipline of transforming data into valuable insights. It helps you understand and predict complex and uncertain phenomena, from pandemics to economics. It also drives many influential technologies today, such as web search, image recognition, and AI assistants. This textbook covers the mathematical foundations and core topics of data science in a comprehensive and rigorous way, including data modeling, statistics, probability, and machine learning. You will learn essential tools, like clustering, dimensionality reduction, and neural networks, as well as how to use them to solve real-world problems with actual datasets and exercises. This book is suitable for professionals, students, and instructors who want to master the theory of data science and explore its applications across various domains. The book requires some prior knowledge of calculus and linear algebra but provides a quick review of these topics in the appendix. About the author Matthias Plaue is a versatile researcher with a background in mathematical physics. He has explored diverse domains, spanning from relativity theory to pedestrian dynamics. As a data scientist, he develops algorithms for data analysis and artificial intelligence, tailored to support strategic decision-making. In addition to his professional pursuits, he has devoted considerable time to mentoring students, imparting a deep understanding of mathematics and its practical application in tackling complex problems across the fields of science, technology, and engineering.

ISBN: 9783662678824$q(electronic bk.)

Standard No.: 10.1007/978-3-662-67882-4doiSubjects--Topical Terms:

181877
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Data sciencean introduction to statistics and machine learning /
LDR:03011nmm a2200337 a 4500 001 647460
003 DE-He213
005 20230831101134.0
006 m d
007 cr nn 008maaau
008 240116s2023 gw s 0 eng d
020 $a 9783662678824$q(electronic bk.)
020 $a 9783662678817$q(paper)
024 7 $a 10.1007/978-3-662-67882-4 $2 doi
035 $a 978-3-662-67882-4
040 $a GP $c GP
041 1 $a eng $h ger
050 4 $a QA276
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .P721 2023
100 1 $a Plaue, Matthias. $3 956843
240 1 0 $a Data Science. $l English
245 1 0 $a Data science $h [electronic resource] : $b an introduction to statistics and machine learning / $c by Matthias Plaue.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2023.
300 $a xxiv, 361 p. : $b ill., digital ; $c 24 cm.
505 0 $a Preface -- Part I Basics -- 1 Elements of data organization -- 2 Descriptive statistics -- Part II Stochastics -- 3 Probability theory -- 4 Inferential statistics -- 5 Multivariate statistics -- Part III Machine learning -- 6 Supervised machine learning -- 7 Unsupervised machine learning -- 8 Applications of machine learning -- Appendix -- A Exercises with answers -- B Mathematical preliminaries -- Supplementary literature -- Index.
520 $a Data science is the discipline of transforming data into valuable insights. It helps you understand and predict complex and uncertain phenomena, from pandemics to economics. It also drives many influential technologies today, such as web search, image recognition, and AI assistants. This textbook covers the mathematical foundations and core topics of data science in a comprehensive and rigorous way, including data modeling, statistics, probability, and machine learning. You will learn essential tools, like clustering, dimensionality reduction, and neural networks, as well as how to use them to solve real-world problems with actual datasets and exercises. This book is suitable for professionals, students, and instructors who want to master the theory of data science and explore its applications across various domains. The book requires some prior knowledge of calculus and linear algebra but provides a quick review of these topics in the appendix. About the author Matthias Plaue is a versatile researcher with a background in mathematical physics. He has explored diverse domains, spanning from relativity theory to pedestrian dynamics. As a data scientist, he develops algorithms for data analysis and artificial intelligence, tailored to support strategic decision-making. In addition to his professional pursuits, he has devoted considerable time to mentoring students, imparting a deep understanding of mathematics and its practical application in tackling complex problems across the fields of science, technology, and engineering.
650 0 $a Mathematical statistics. $3 181877
650 0 $a Machine learning. $3 188639
650 1 4 $a Data Science. $3 913495
650 2 4 $a Statistics. $3 182057
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-662-67882-4
950 $a Computer Science (SpringerNature-11645)