東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A first course in statistical learni...

Lederer, Johannes.

FindBook

Google Book

Amazon

博客來

A first course in statistical learning = with data examples and Python code /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A first course in statistical learning/ by Johannes Lederer.
其他題名:	with data examples and Python code /
作者:	Lederer, Johannes.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xiv, 282 p. :ill. (chiefly color), digital ;24 cm.
內容註:	Part I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning.
Contained By:	Springer Nature eBook
標題:	Statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-30276-3
ISBN:	9783031302763

A first course in statistical learning = with data examples and Python code /
Lederer, Johannes.

A first course in statistical learningwith data examples and Python code /[electronic resource] :by Johannes Lederer. - Cham :Springer Nature Switzerland :2025. - xiv, 282 p. :ill. (chiefly color), digital ;24 cm. - Statistics and computing,2197-1706. - Statistics and computing..

Part I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning.

This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning. The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage. In addition, the book has the following features: A careful selection of topics ensures rapid progress. An opening question at the beginning of each chapter leads the reader through the topic. Expositions are rigorous yet based on elementary mathematics. More than two hundred exercises help digest the material. A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.

ISBN: 9783031302763

Standard No.: 10.1007/978-3-031-30276-3doiSubjects--Topical Terms:

517247
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

A first course in statistical learning = with data examples and Python code /
LDR:03045nmm a2200361 a 4500 001 2409435
003 DE-He213
005 20250225084055.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031302763 $q (electronic bk.)
020 $a 9783031302756 $q (paper)
024 7 $a 10.1007/978-3-031-30276-3 $2 doi
035 $a 978-3-031-30276-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a UYQM $2 bicssc
072 7 $a COM077000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a UYQM $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .L473 2025
100 1 $a Lederer, Johannes. $3 3591022
245 1 2 $a A first course in statistical learning $h [electronic resource] : $b with data examples and Python code / $c by Johannes Lederer.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xiv, 282 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Statistics and computing, $x 2197-1706
505 0 $a Part I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning.
520 $a This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning. The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage. In addition, the book has the following features: A careful selection of topics ensures rapid progress. An opening question at the beginning of each chapter leads the reader through the topic. Expositions are rigorous yet based on elementary mathematics. More than two hundred exercises help digest the material. A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.
650 0 $a Statistics. $3 517247
650 1 4 $a Statistical Learning. $3 3597795
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Statistical Software. $3 3596845
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Applied Statistics. $3 3300946
650 2 4 $a Data Science. $3 3538937
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Statistics and computing. $3 1566755
856 4 0 $u https://doi.org/10.1007/978-3-031-30276-3
950 $a Mathematics and Statistics (SpringerNature-11649)