東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Statistical learning with math and P...

Suzuki, Joe.

Linked to FindBook

Google Book

Amazon

博客來

Statistical learning with math and Python = 100 exercises for building logic /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical learning with math and Python/ by Joe Suzuki.
Reminder of title:	100 exercises for building logic /
Author:	Suzuki, Joe.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xi, 256 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
Contained By:	Springer Nature eBook
Subject:	Mathematical statistics. -
Online resource:	https://doi.org/10.1007/978-981-15-7877-9
ISBN:	9789811578779

Statistical learning with math and Python = 100 exercises for building logic /
Suzuki, Joe.

Statistical learning with math and Python100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Singapore :2021. - xi, 256 p. :ill. (some col.), digital ;24 cm.

Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

ISBN: 9789811578779

Standard No.: 10.1007/978-981-15-7877-9doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276 / .S89 2021

Dewey Class. No.: 519.5

Statistical learning with math and Python = 100 exercises for building logic /
LDR:02638nmm a2200325 a 4500 001 2242086
003 DE-He213
005 20210803151749.0
006 m d
007 cr nn 008maaau
008 211207s2021 si s 0 eng d
020 $a 9789811578779 $q (electronic bk.)
020 $a 9789811578762 $q (paper)
024 7 $a 10.1007/978-981-15-7877-9 $2 doi
035 $a 978-981-15-7877-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276 $b .S89 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .S968 2021
100 1 $a Suzuki, Joe. $3 2165769
245 1 0 $a Statistical learning with math and Python $h [electronic resource] : $b 100 exercises for building logic / $c by Joe Suzuki.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xi, 256 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
520 $a The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
650 0 $a Mathematical statistics. $3 516858
650 0 $a Logic, Symbolic and mathematical. $3 532051
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-15-7877-9
950 $a Computer Science (SpringerNature-11645)