08-12-2026, 02:55 PM
Automated Machine Learning
BY Frank Hutter
Summary
Automated Machine Learning: Methods, Systems, Challenges (2019), edited by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, provides a comprehensive introduction to Automated Machine Learning (AutoML), which aims to reduce the need for human experts to manually select machine-learning algorithms, architectures, preprocessing methods, and hyperparameters.
The book explains key AutoML techniques such as hyperparameter optimization, meta-learning, and neural architecture search, and examines practical systems including Auto-WEKA, Hyperopt-Sklearn, auto-sklearn, TPOT, and the Automatic Statistician. It also reviews the results of international AutoML competitions, highlighting the strengths and limitations of different approaches. Overall, the book presents AutoML as a way to make machine learning more accessible, efficient, and increasingly automated for researchers, students, and practitioners.
BOOK [FREE AS A PDF]
BY Frank Hutter
Summary
Automated Machine Learning: Methods, Systems, Challenges (2019), edited by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, provides a comprehensive introduction to Automated Machine Learning (AutoML), which aims to reduce the need for human experts to manually select machine-learning algorithms, architectures, preprocessing methods, and hyperparameters.
The book explains key AutoML techniques such as hyperparameter optimization, meta-learning, and neural architecture search, and examines practical systems including Auto-WEKA, Hyperopt-Sklearn, auto-sklearn, TPOT, and the Automatic Statistician. It also reviews the results of international AutoML competitions, highlighting the strengths and limitations of different approaches. Overall, the book presents AutoML as a way to make machine learning more accessible, efficient, and increasingly automated for researchers, students, and practitioners.
BOOK [FREE AS A PDF]
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