建國科技大學圖書館 |

語系: 繁體中文

說明(常見問題)

圖書館個人資料蒐集告知聲明

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning for signal processi...

Little, Max A.

Machine learning for signal processing : data science, algorithms, and computational statistics

紀錄類型:	書目-語言資料,印刷品 : 單行本
副題名:	data science, algorithms, and computational statistics
作者:	LittleMax A.,
出版地:	Oxford
出版者:	Oxford University Press;
出版年:	2019.
面頁冊數:	xviii, 359 p.ill. (some col.) : 26 cm.;
標題:	Machine learning - Mathematics. -
標題:	Signal processing - Digital techniques. -
標題:	Signal processing - Mathematics. -
摘要註:	"This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications. Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic."--Back cover.
ISBN:	9780198714934

Machine learning for signal processing : data science, algorithms, and computational statistics
Little, Max A.

Machine learning for signal processing : data science, algorithms, and computational statistics / Max A. Little - Oxford : Oxford University Press, 2019.. - xviii, 359 p. ; ill. (some col.) ; 26 cm..
Includes bibliographical references (p. 245-352) and index..
ISBN 9780198714934ISBN 0198714939
Machine learningSignal processingSignal processing -- Mathematics. -- Digital techniques. -- Mathematics.

Machine learning for signal processing : data science, algorithms, and computational statistics
LDR:02594cam a2200217 450 001 393637
010 1 $a 9780198714934 $b hbk. $d NT1680
010 1 $a 0198714939 $b hbk.
010 1 $z 9780191024313 $b ebk.
010 1 $z 9780191879180 $b ebk.
100 $a 20210910d2019 k y0engy50 b
101 0 $a eng
102 $a gb
105 $a a a 001yy
200 1 $a Machine learning for signal processing $e data science, algorithms, and computational statistics $f Max A. Little
210 $a Oxford $a New York, NY $c Oxford University Press $d 2019.
215 1 $a xviii, 359 p. $c ill. (some col.) $d 26 cm.
320 $a Includes bibliographical references (p. 245-352) and index.
330 $a "This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications. Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic."--Back cover.
606 $a Machine learning $x Mathematics. $2 lc $3 385670
606 $a Signal processing $x Digital techniques. $2 lc $3 12540
606 $a Signal processing $x Mathematics. $2 lc $3 26656
676 $a 006.310151 $v 23
680 $a Q325.5 $b .L58 2019
700 1 $a Little $b Max A. $3 385671
801 0 $a cw $b CTU $c 20210910 $g AACR2