國立虎尾科技大學 |

Machine learning refined : = foundations, algorithms, and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning refined :/ Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos.
Reminder of title:	foundations, algorithms, and applications /
Author:	Watt, Jeremy,
other author:	Borhani, Reza,
Description:	1 online resource (xiii, 286 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 06 Sep 2016).
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1017/CBO9781316402276
ISBN:	9781316402276 (ebook)

Machine learning refined : = foundations, algorithms, and applications /
Watt, Jeremy,

Machine learning refined :foundations, algorithms, and applications /Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos. - 1 online resource (xiii, 286 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 06 Sep 2016).

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.

ISBN: 9781316402276 (ebook)Subjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .W38 2016

Dewey Class. No.: 006.3/1

Machine learning refined : = foundations, algorithms, and applications /
LDR:01973nam a2200277 i 4500 001 1123899
003 UkCbUP
005 20160906032342.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2016||||enk o ||1 0|eng|d
020 $a 9781316402276 (ebook)
020 $z 9781107123526 (hardback)
035 $a CR9781316402276
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 0 0 $a Q325.5 $b .W38 2016
082 0 0 $a 006.3/1 $2 23
100 1 $a Watt, Jeremy, $e author. $3 1441111
245 1 0 $a Machine learning refined : $b foundations, algorithms, and applications / $c Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos.
264 1 $a Cambridge : $b Cambridge University Press, $c 2016.
300 $a 1 online resource (xiii, 286 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Title from publisher's bibliographic system (viewed on 06 Sep 2016).
520 $a Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.
650 0 $a Machine learning. $3 561253
700 1 $a Borhani, Reza, $e author. $3 1441112
700 1 $a Katsaggelos, Aggelos Konstantinos, $d 1956- $3 598532
776 0 8 $i Print version: $z 9781107123526
856 4 0 $u https://doi.org/10.1017/CBO9781316402276