Structured data emerges rapidly in a large number of disciplines: bioinformatics, systems biology, social network analysis, natural language processing and the Internet generate large collections of strings, graphs, trees, and time series. Designing and analysing algorithms for dealing with these large collections of structured data has turned into a major focus of machine learning over recent years, both in the input and output domain of machine learning algorithms, and is starting to enable exciting new applications of machine learning. The goal of this workshop is to bring together experts on learning with structured input and structured output domains and its applications, in order to exchange the latest developments in these growing fields. The workshop will include one session on learning with structured inputs, featuring a keynote by Prof. Eric Xing from Carnegie Mellon University. A second session will focus on learning with structured outputs, with a keynote by Dr. Yasemin Altun from the MPI for Biological Cybernetics. A third session will present novel applications of structured input-structured output learning to real-world problems.