Machine learning (ML) – the pursuit of computational methods for making predictions and decisions from data -- plays a central role in our information society. The Institute for Machine Learning at ETH Zurich spans research topics from theoretical foundations of statistical learning to the development of novel machine learning algorithms and their application in interdisciplinary research projects, in domains ranging from health sciences to biology to environmental science and engineering. Past accomplishments include pioneering contributions to a wide range of topics such as model selection and validation, combinatorial optimization, active learning, probabilistic modeling, structured prediction, etc. Motivated by challenges arising from massive data sets and the ubiquitous deployment of machine learning in real world systems, current research addresses foundational issues on topics including large-scale machine learning (e.g., scaling learning algorithms to big data sets in a principled and resource efficient manner), interactive machine learning (e.g., understanding learning systems that interact with their environment, affecting the data they see) and the information-theoretic analysis of algorithms (e.g., understanding what it means for an algorithm to “generalize”). This fundamental research is strongly motivated by applications of ML in other areas of computer science (such as natural language processing, computer vision, HCI, medical data analysis, security, programming languages), as well as active collaborations with other academic disciplines and industry. The Institute for Machine Learning organizes the Center for Learning Systems that is founded as a joint research unit for graduate education by ETH Zurich and the Max Planck Society.