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Machine Learning Refined
Foundations, Algorithms, and Applications

$68.00 ( ) USD

  • Date Published: April 2016
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact eBooks.com for availability.
  • format: Adobe eBook Reader
  • isbn: 9781316560365

$ 68.00 USD ( )
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About the Authors
  • 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.

    • Provides MATLAB-based coding exercises, real-world examples, and practical applications
    • Takes a unique approach, enabling a more coherent, intuitive, and interactive way of learning
    • Includes over 150 illustrations, many of which are in full colour
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    Product details

    • Date Published: April 2016
    • format: Adobe eBook Reader
    • isbn: 9781316560365
    • contains: 135 colour illus. 3 tables 81 exercises
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact eBooks.com for availability.
  • 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.

  • Resources for

    Machine Learning Refined

    Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos

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  • Authors

    Jeremy Watt, Northwestern University, Illinois
    Jeremy Watt received his PhD in Computer Science and Electrical Engineering from Northwestern University, Illinois. His research interests lie in machine learning and computer vision, as well as numerical optimization.

    Reza Borhani, Northwestern University, Illinois
    Reza Borhani received his PhD in Computer Science and Electrical Engineering from Northwestern University, Illinois. His research interests lie in the design and analysis of algorithms for problems in machine learning and computer vision.

    Aggelos K. Katsaggelos, Northwestern University, Illinois
    Aggelos K. Katsaggelos is a professor and holder of the AT&T chair in the Department of Electrical Engineering and Computer Science at Northwestern University, Illinois, where he also heads the Image and Video Processing Laboratory.

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