To achieve state-of-the-art performance with machine learning (ML), users, developers and researchers have to make many design decisions, incl. ML algorithm, pre-processing, post-processing and their hyperparameters. The course on automated machine learning (AutoML) at the ai-campus.org conveys the basics on how to make these design decisions automatically, s.t. ML practitioners can efficiently apply ML to new applications. The course covers e.g. hyperparameter optimization, Bayesian optimization, neural architecture search and meta-learning.