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Table of contents

  1. Front Matter
    Pages i-xiv
  2. AutoML Methods

    1. Front Matter
      Pages 1-1
    2. Matthias Feurer, Frank Hutter
      Pages 3-33 Open Access
    3. Joaquin Vanschoren
      Pages 35-61 Open Access
    4. Thomas Elsken, Jan Hendrik Metzen, Frank Hutter
      Pages 63-77 Open Access
  3. AutoML Systems

    1. Front Matter
      Pages 79-79
    2. Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown
      Pages 81-95 Open Access
    3. Brent Komer, James Bergstra, Chris Eliasmith
      Pages 97-111 Open Access
    4. Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter
      Pages 113-134 Open Access
    5. Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart et al.
      Pages 135-149 Open Access
    6. Randal S. Olson, Jason H. Moore
      Pages 151-160 Open Access
    7. Christian Steinruecken, Emma Smith, David Janz, James Lloyd, Zoubin Ghahramani
      Pages 161-173 Open Access
  4. AutoML Challenges

    1. Front Matter
      Pages 175-175
    2. Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu et al.
      Pages 177-219 Open Access

About this book

Introduction

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Keywords

Machine learning Automated machine learning Automated data science Off-the-shelf machine learning Machine learning software Selecting a machine learning algorithm Tuning Hyperparameters Feature selection Preprocessing Deep learning Architecture search Machine learning pipeline optimization Open Access

Editors and affiliations

  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany
  2. 2.University of WyomingLaramieUSA
  3. 3.Eindhoven University of TechnologyEindhovenThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-05318-5
  • Copyright Information The Editor(s) (if applicable) and The Author(s) 2019
  • License CC BY
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-05317-8
  • Online ISBN 978-3-030-05318-5
  • Series Print ISSN 2520-131X
  • Series Online ISSN 2520-1328
  • Buy this book on publisher's site
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