Automated Machine Learning: Methods, Systems, Challenges (The Springer Series on Challenges in Machine Learning) - Hardcover

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9783030053178: Automated Machine Learning: Methods, Systems, Challenges (The Springer Series on Challenges in Machine Learning)
Hardcover
ISBN 10: 3030053172 ISBN 13: 9783030053178
Publisher: Springer, 2019

Synopsis

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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From the Back Cover

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.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2019
Language
English
ISBN 10 
3030053172
ISBN 13 
9783030053178
Binding
Hardcover
Edition number
1
Number of pages
233
Editor
Hutter Frank, Kotthoff Lars, Vanschoren Joaquin
Rating
  • 4.03 out of 5 stars
    33 ratings by Goodreads

Other Popular Editions of the Same Title

9783030053192: Automated Machine Learning: Methods, Systems, Challenges

Featured Edition

ISBN 10:  3030053199 ISBN 13:  9783030053192
Publisher: Springer, 2019
Softcover