國立虎尾科技大學 |

Machine Learning and Artificial Intelligence

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning and Artificial Intelligence/ by Ameet V Joshi.
Author:	Joshi, Ameet V.
Description:	XXII, 261 p. 98 illus., 94 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Electrical engineering. -
Online resource:	https://doi.org/10.1007/978-3-030-26622-6
ISBN:	9783030266226

Machine Learning and Artificial Intelligence
Joshi, Ameet V.

Machine Learning and Artificial Intelligence[electronic resource] /by Ameet V Joshi. - 1st ed. 2020. - XXII, 261 p. 98 illus., 94 illus. in color.online resource.

Introduction -- Part I Introduction to AI and ML -- Essential concepts in AL and ML -- Part II Techniques for Static Machine Learning Models -- Perceptron and Neural Networks -- Decision Trees -- Advanced Decision Trees -- Support Vector Machines -- Probabilistic Models -- Deep Learning -- Part III Techniques for Dynamic Machine Learning Models -- Autoregressive and Moving Average Models -- Hidden Markov Models and Conditional Random Fields -- Recurrent Neural Networks -- Part IV Applications -- Classification Regression -- Ranking -- Clustering -- Recommendations -- Next Best Actions -- Designing ML Pipelines -- Using ML Libraries -- Azure Machine Learning Studio -- Conclusions.

This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.

ISBN: 9783030266226

Standard No.: 10.1007/978-3-030-26622-6doiSubjects--Topical Terms:

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Machine Learning and Artificial Intelligence
LDR:03224nam a22003975i 4500 001 1018634
003 DE-He213
005 20200703075618.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030266226 $9 978-3-030-26622-6
024 7 $a 10.1007/978-3-030-26622-6 $2 doi
035 $a 978-3-030-26622-6
050 4 $a TK1-9971
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Joshi, Ameet V. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1313677
245 1 0 $a Machine Learning and Artificial Intelligence $h [electronic resource] / $c by Ameet V Joshi.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXII, 261 p. 98 illus., 94 illus. in color. $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
505 0 $a Introduction -- Part I Introduction to AI and ML -- Essential concepts in AL and ML -- Part II Techniques for Static Machine Learning Models -- Perceptron and Neural Networks -- Decision Trees -- Advanced Decision Trees -- Support Vector Machines -- Probabilistic Models -- Deep Learning -- Part III Techniques for Dynamic Machine Learning Models -- Autoregressive and Moving Average Models -- Hidden Markov Models and Conditional Random Fields -- Recurrent Neural Networks -- Part IV Applications -- Classification Regression -- Ranking -- Clustering -- Recommendations -- Next Best Actions -- Designing ML Pipelines -- Using ML Libraries -- Azure Machine Learning Studio -- Conclusions.
520 $a This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.
650 0 $a Electrical engineering. $3 596380
650 0 $a Machine learning. $3 561253
650 0 $a Computational intelligence. $3 568984
650 0 $a Data mining. $3 528622
650 0 $a Information storage and retrieval. $3 1069252
650 0 $a Big data. $3 981821
650 1 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Information Storage and Retrieval. $3 593926
650 2 4 $a Big Data/Analytics. $3 1106909
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030266219
776 0 8 $i Printed edition: $z 9783030266233
776 0 8 $i Printed edition: $z 9783030266240
856 4 0 $u https://doi.org/10.1007/978-3-030-26622-6
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)