Automated Machine Learning

1. Front Matter

   Pages i-xiv

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2. AutoML Methods

   1. Front Matter

      Pages 1-1

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   2. Hyperparameter Optimization

      • Matthias Feurer, Frank Hutter

      Pages 3-33Open Access

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   3. Meta-Learning

      • Joaquin Vanschoren

      Pages 35-61Open Access

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   4. Neural Architecture Search

      • Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

      Pages 63-77Open Access

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3. AutoML Systems

   1. Front Matter

      Pages 79-79

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   2. Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA

      • Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown

      Pages 81-95Open Access

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   3. Hyperopt-Sklearn

      • Brent Komer, James Bergstra, Chris Eliasmith

      Pages 97-111Open Access

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   4. Auto-sklearn: Efficient and Robust Automated Machine Learning

      • Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, Frank Hutter

      Pages 113-134Open Access

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   5. Towards Automatically-Tuned Deep Neural Networks

      • Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart et al.

      Pages 135-149Open Access

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   6. TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning

      • Randal S. Olson, Jason H. Moore

      Pages 151-160Open Access

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   7. The Automatic Statistician

      • Christian Steinruecken, Emma Smith, David Janz, James Lloyd, Zoubin Ghahramani

      Pages 161-173Open Access

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4. AutoML Challenges

   1. Front Matter

      Pages 175-175

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   2. Analysis of the AutoML Challenge Series 2015–2018

      • Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu et al.

      Pages 177-219Open Access

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5. Correction to: Neural Architecture Search

   • Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

   Pages C1-C1Open Access

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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. 

• 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

“This interesting collection should be useful for AutoML researchers seeking an overview and comprehensive bibliography.” (Anoop Malaviya, Computing Reviews, June 14, 2021)

• 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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