The summer school includes lectures on predictive modelling methods for big and complex data. More specifically, the lectures present methods handling the following complexity aspects: (a) structured data as input or output of the prediction process, (b) very large/massive datasets, with many examples and/or many input/output dimensions, where data may be streaming at high rates, (c) incompletely/partially labelled data, and (d) data placed in a spatio-temporal or network context. Each of these is a major challenge to current ML/DM approaches and is the central topic of active research in areas such as structured-output prediction, mining data streams, semi-supervised learning, and mining network data. The applicability and the potential of the presented methods will be demonstrated on several showcases from molecular biology, sensor networks, multimedia, and social networks.