Modern learning systems, such as the recent deep learning, reinforcement learning, and probabilistic inference architectures, have become increasingly complex, often beyond the human ability to comprehend them. Such complexity is important: The more complex these systems are, the more powerful they often are. A new research problem has therefore emerged: How can the complexity, i.e. the design, components, and hyperparameters, be configured automatically so that these systems perform as well as possible? This is the problem of metalearning. Several approaches have emerged, including those based on Bayesian optimization, gradient descent, reinforcement learning, and evolutionary computation.