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Princeton University Library Catalog
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Machine learning refined : foundations, algorithms, and applications / Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos.
Author
Watt, Jeremy
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Format
Book
Language
English
Published/Created
Cambridge, United Kingdom : Cambridge University Press, 2016.
Description
xiii, 286 pages : illustrations ; 26 cm
Availability
Copies in the Library
Location
Call Number
Status
Location Service
Notes
Engineering Library - Stacks
Q325.5 .W38 2016
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Details
Subject(s)
Machine learning
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Author
Borhani, Reza
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Katsaggelos, Aggelos Konstantinos, 1956-
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Library of Congress genre(s)
Instructional and educational works
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Summary note
"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.
Bibliographic references
Includes bibliographical references and index.
Contents
Introduction
Part I. Fundamental tools and concepts ; Fundamentals of numerical optimization
Regression
Classification
Part II. Tools for fully data-driven machine learning ; Automatic feature design for regression
Automatic feature design for classification
Kernels, backpropagation, and regularized cross-validation
Part III. Methods for large scale machine learning ; Advanced gradient schemes
Dimension reduction techniques
Part IV. Appendices ; Appendix A. Basic vector and matrix operations
Appendix B. Basics of vector calculus
Appendix C. Fundamental matrix factorizations and the pseudo-inverse
Appendix D. Convex geometry.
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Other title(s)
Foundations, algorithms, and applications
ISBN
9781107123526 ((hardback))
1107123526 ((hardback))
LCCN
2015041122
OCLC
927438826
Statement on language in description
Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage.
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