國立高雄大學圖資館 |

Language: English

Back

Machine learning with R

Ghatak, Abhijit.

Machine learning with R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning with Rby Abhijit Ghatak.
Author:	Ghatak, Abhijit.
Published:	Singapore :Springer Singapore :2017.
Description:	xix, 210 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-981-10-6808-9
ISBN:	9789811068089$q(electronic bk.)

Machine learning with R
Ghatak, Abhijit.

Machine learning with R[electronic resource] /by Abhijit Ghatak. - Singapore :Springer Singapore :2017. - xix, 210 p. :ill., digital ;24 cm.

This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.

ISBN: 9789811068089$q(electronic bk.)

Standard No.: 10.1007/978-981-10-6808-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning with R
LDR:02296nmm a2200313 a 4500 001 525051
003 DE-He213
005 20171124212327.0
006 m d
007 cr nn 008maaau
008 180904s2017 si s 0 eng d
020 $a 9789811068089$q(electronic bk.)
020 $a 9789811068072$q(paper)
024 7 $a 10.1007/978-981-10-6808-9 $2 doi
035 $a 978-981-10-6808-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .G411 2017
100 1 $a Ghatak, Abhijit. $3 797143
245 1 0 $a Machine learning with R $h [electronic resource] / $c by Abhijit Ghatak.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2017.
300 $a xix, 210 p. : $b ill., digital ; $c 24 cm.
520 $a This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.
650 0 $a Machine learning. $3 188639
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Programming Techniques. $3 274470
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 274102
650 2 4 $a Database Management. $3 273994
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-6808-9
950 $a Computer Science (Springer-11645)