The success of machine learning crucially relies on human machine learning experts, who construct appropriate features and workflows, and select appropriate machine learning paradigms, algorithms, neural architectures, and their hyperparameters. Automatic machine learning (AutoML) is an emerging research area that targets the progressive automation of machine learning, which uses machine learning and optimization to develop off-the-shelf machine learning methods that can be used easily and without expert knowledge. It covers a broad range of subfields, including hyperparameter optimization, neural architecture search, meta-learning, and transfer learning. This tutorial will cover the methods underlying the current state of the art in this fast-paced field.