Machine learning (ML) has achieved considerable successes in recent years, but this success often relies on human experts, who construct appropriate features, design learning architectures, set their hyperparameters, and develop new learning algorithms. Driven by the demand for robust, off-the-shelf ML methods from an ever-growing community, the research area of AutoML targets the progressive automation of machine learning aiming to make effective methods available to everyone. Hence, the workshop targets a broad audience ranging from core ML researchers in different fields of ML connected to AutoML, such as neural architecture search (NAS), hyperparameter optimization, meta-learning, and learning-to-learn, to domain experts aiming to apply ML to new types of problems.