Machine learning refined

Machine learning refined: foundations, algorithms, and applications

by Watt, Jeremy

Type Book
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"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 understa…

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Title Statements

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

Summary

"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"--

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Edition Statements

  • Publication Date:2016.

Publication Statements

The Physical Object

  • Description:xiii, 286 pages : : illustrations ; : 26 cm

Edition Identifiers

  • ISBN:9781107123526, 1107123526
  • oclc:ocn927438826
  • LC Classification:Q325.5
  • Dewey Decimal:006.3/1

Table of Contents

Fundamentals of numerical optimization -- Regression -- Classification -- Automatic feature design for regression -- Automatic feature design for classification -- kernels, backpropagation, and regularized cross-validation -- Advanced gradient schemes -- Dimension reduction techniques -- 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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Subjects

Apprentissage automatique Computers and IT Machine learning Maschinelles Lernen

Writers

Watt, Jeremy Borhani, Reza Katsaggelos, Aggelos Konstantinos

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