Book Details


Automated Machine Learning

Publication year: 2019

ISBN: 978-3-030-05318-5

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This 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.

Subject: Computer Science, 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, Artificial Intelligence, Image Processing and Computer, Vision, Pattern Recognition