Overview
- 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
Part of the book series: The Springer Series on Challenges in Machine Learning (SSCML)
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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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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
Table of contents (11 chapters)
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Front Matter
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AutoML Methods
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Front Matter
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AutoML Systems
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Front Matter
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AutoML Challenges
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Front Matter
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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
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