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.
