HOME > Detail View

Detail View

Machine learning refined : foundations, algorithms, and applications

Machine learning refined : foundations, algorithms, and applications (Loan 6 times)

Material type
단행본
Personal Author
Watt, Jeremy. Borhani, Reza. Katsaggelos, Aggelos Konstantinos, 1956-.
Title Statement
Machine learning refined : foundations, algorithms, and applications / Jeremy Watt, Reza Borhani, Aggelos Katsaggelos.
Publication, Distribution, etc
New York :   Cambridge University Press,   2016.  
Physical Medium
xiii, 286 p. : ill. (some col.) ; 26 cm.
ISBN
9781107123526 (hardback)
요약
"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"--Provided by publisher.
Bibliography, Etc. Note
Includes bibliographical references (p. [280]-284) and index.
Subject Added Entry-Topical Term
Machine learning.
000 00000cam u2200205 a 4500
001 000045902448
005 20170407105314
008 170407s2016 nyua b 001 0 eng d
010 ▼a 2015041122
020 ▼a 9781107123526 (hardback)
035 ▼a (KERIS)BIB000014467585
040 ▼a 211052 ▼b 211052 ▼c 211052 ▼e 211052 ▼d 211052 ▼d 211009
050 0 0 ▼a Q325.5 ▼b .W38 2016
082 0 4 ▼a 006.31 ▼2 22
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b M1494
245 0 0 ▼a Machine learning refined : ▼b foundations, algorithms, and applications / ▼c Jeremy Watt, Reza Borhani, Aggelos Katsaggelos.
260 ▼a New York : ▼b Cambridge University Press, ▼c 2016.
300 ▼a xiii, 286 p. : ▼b ill. (some col.) ; ▼c 26 cm.
504 ▼a Includes bibliographical references (p. [280]-284) and index.
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"--Provided by publisher.
650 0 ▼a Machine learning.
700 1 ▼a Watt, Jeremy.
700 1 ▼a Borhani, Reza.
700 1 ▼a Katsaggelos, Aggelos Konstantinos, ▼d 1956-.

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Medical Library/Monographs(3F)/ Call Number 006.31 M1494 Accession No. 131051556 Availability In loan Due Date 2021-09-02 Make a Reservation Available for Reserve R Service

Contents information

Table of Contents

1. Introduction; Part I. The Basics: 2. Fundamentals of numerical optimization; 3. Knowledge-driven regression; 4. Knowledge-driven classification; Part II. Automatic Feature Design: 5. Automatic feature design for regression; 6. Automatic feature design for classification; 7. Kernels, backpropagation, and regularized cross-validation; Part III. Tools for Large Scale Data: 8. Advanced gradient schemes; 9. Dimension reduction techniques; Part IV. Appendices.


Information Provided By: : Aladin

New Arrivals Books in Related Fields

Baumer, Benjamin (2021)