Automated Machine Learning

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.

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

Inhalt
1 Hyperparameter Optimization.- 2 Meta-Learning.- 3 Neural Architecture Search.- 4 Auto-WEKA.- 5 Hyperopt-Sklearn.- 6 Auto-sklearn.- 7 Towards Automatically-Tuned Deep Neural Networks.- 8 TPOT.- 9 The Automatic Statistician.- 10 AutoML Challenges.