Machine learning refined : foundations, algorithms, and applications / Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos.

Author
Watt, Jeremy [Browse]
Format
Book
Language
English
Published/​Created
Cambridge, United Kingdom : Cambridge University Press, 2016.
Description
xiii, 286 pages : illustrations ; 26 cm

Availability

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Location Call Number Status Location Service Notes
Engineering Library - Stacks Q325.5 .W38 2016 Browse related items Request

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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.
    Other title(s)
    Foundations, algorithms, and applications
    ISBN
    • 9781107123526 ((hardback))
    • 1107123526 ((hardback))
    LCCN
    2015041122
    OCLC
    927438826
    Statement on language in description
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