國立高雄大學圖資館 |

Language: English

Back

MATLAB machine learning recipesa pro...

Paluszek, Michael.

MATLAB machine learning recipesa problem-solution approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	MATLAB machine learning recipesby Michael Paluszek, Stephanie Thomas.
Reminder of title:	a problem-solution approach /
Author:	Paluszek, Michael.
other author:	Thomas, Stephanie.
Published:	Berkeley, CA :Apress :2019.
Description:	xix, 347 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-3916-2
ISBN:	9781484239162$q(electronic bk.)

MATLAB machine learning recipesa problem-solution approach /
Paluszek, Michael.

MATLAB machine learning recipesa problem-solution approach /[electronic resource] :by Michael Paluszek, Stephanie Thomas. - 2nd ed. - Berkeley, CA :Apress :2019. - xix, 347 p. :ill., digital ;24 cm.

1 Overview -- 2 Data Representation -- 3 MATLAB Graphics -- 4 Kalman Filters -- 5 Adaptive Control -- 6 Fuzzy Logic -- 7 Data Classification with Decision Trees -- 8 Simple Neural Nets -- 9 Classification with Neural Nets -- 10 Neural Nets with Deep Learning -- 11 Neural Aircraft Control -- 12 Multiple Hypothesis Testing -- 13 Autonomous Driving with MHT -- 14 Case-Based Expert Systems -- Appendix A: A Brief History of Autonomous Learning -- Appendix B: Software for Machine Learning.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. You will: Learn to write code for machine learning, adaptive control and estimation using MATLAB See how these three areas complement each other Understand why these three areas are needed for robust machine learning applications Use MATLAB graphics and visualization tools for machine learning Code real world examples in MATLAB for major applications of machine learning in big data.

ISBN: 9781484239162$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-3916-2doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .P358 2019

Dewey Class. No.: 006.31

MATLAB machine learning recipesa problem-solution approach /
LDR:02561nmm a2200337 a 4500 001 553012
003 DE-He213
005 20190813161326.0
006 m d
007 cr nn 008maaau
008 191107s2019 cau s 0 eng d
020 $a 9781484239162$q(electronic bk.)
020 $a 9781484239155$q(paper)
024 7 $a 10.1007/978-1-4842-3916-2 $2 doi
035 $a 978-1-4842-3916-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .P358 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .P184 2019
100 1 $a Paluszek, Michael. $3 732019
245 1 0 $a MATLAB machine learning recipes $h [electronic resource] : $b a problem-solution approach / $c by Michael Paluszek, Stephanie Thomas.
250 $a 2nd ed.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xix, 347 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 Overview -- 2 Data Representation -- 3 MATLAB Graphics -- 4 Kalman Filters -- 5 Adaptive Control -- 6 Fuzzy Logic -- 7 Data Classification with Decision Trees -- 8 Simple Neural Nets -- 9 Classification with Neural Nets -- 10 Neural Nets with Deep Learning -- 11 Neural Aircraft Control -- 12 Multiple Hypothesis Testing -- 13 Autonomous Driving with MHT -- 14 Case-Based Expert Systems -- Appendix A: A Brief History of Autonomous Learning -- Appendix B: Software for Machine Learning.
520 $a Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. You will: Learn to write code for machine learning, adaptive control and estimation using MATLAB See how these three areas complement each other Understand why these three areas are needed for robust machine learning applications Use MATLAB graphics and visualization tools for machine learning Code real world examples in MATLAB for major applications of machine learning in big data.
650 0 $a Machine learning. $3 188639
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Big Data. $3 760530
700 1 $a Thomas, Stephanie. $3 210618
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-3916-2
950 $a Professional and Applied Computing (Springer-12059)