Automated Machine Learning (original) (raw)
Overview
Editors:
- Frank Hutter
- Department of Computer Science, University of Freiburg, Freiburg, Germany
- Lars Kotthoff
- University of Wyoming, Laramie, USA
- Joaquin Vanschoren
- Eindhoven University of Technology, Eindhoven, The Netherlands
Presents a tutorial-level overview of the methods underlying automatic machine learning, enabling readers to easily understand the key concepts behind AutoML
Offers a comprehensive collection of in-depth descriptions of AutoML systems, allowing readers to see how the key concepts have been implemented in the context of actual systems
Discusses an independent international competition of many different systems, providing an independent evaluation of pros and cons of different AutoML approaches
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About this book
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.
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Table of contents (11 chapters)
AutoML Methods
AutoML Systems
AutoML Challenges
Reviews
“This interesting collection should be useful for AutoML researchers seeking an overview and comprehensive bibliography.” (Anoop Malaviya, Computing Reviews, June 14, 2021)
Editors and Affiliations
Department of Computer Science, University of Freiburg, Freiburg, Germany
Frank Hutter
University of Wyoming, Laramie, USA
Lars Kotthoff
Eindhoven University of Technology, Eindhoven, The Netherlands
Joaquin Vanschoren
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Bibliographic Information
- Book Title: Automated Machine Learning
- Book Subtitle: Methods, Systems, Challenges
- Editors: Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
- Series Title: The Springer Series on Challenges in Machine Learning
- DOI: https://doi.org/10.1007/978-3-030-05318-5
- Publisher: Springer Cham
- eBook Packages: Computer Science, Computer Science (R0)
- Copyright Information: The Editor(s) (if applicable) and The Author(s) 2019
- Hardcover ISBN: 978-3-030-05317-8Published: 28 May 2019
- eBook ISBN: 978-3-030-05318-5Published: 17 May 2019
- Series ISSN: 2520-131X
- Series E-ISSN: 2520-1328
- Edition Number: 1
- Number of Pages: XIV, 219
- Number of Illustrations: 9 b/w illustrations, 45 illustrations in colour
- Topics: Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition