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. 

Table of Contents

Part I AutoML Methods

1 Hyperparameter Optimization

2 Meta-Learning

3 Neural Architecture Search

Part II AutoML Systems

4 Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA

5 Hyperopt-Sklearn

6 Auto-sklearn: Efficient and Robust Automated MachineLearning

7 Towards Automatically-Tuned Deep Neural Networks

8 TPOT: A Tree-Based Pipeline Optimization Toolfor Automating Machine Learning

9 The Automatic Statistician

Part Ill AutoML Challenges

10 Analysis of the AutoM L Challenge Series 2015-2018