The task of constructing composite systems, that is systems composed of more than one part, can be seen as interdisciplinary area which builds on expertise in different domains. The aim of this workshop is to explore the possibilities of constructing such systems with the aid of Machine Learning and exploiting the know-how of Data Mining. One way of producing composite systems is by inducing the constituents and then by putting the individual parts together. For instance, a text extraction system may be composed of various subsystems, some oriented towards tagging, morphosyntactic analysis or word sense disambigua- tion. This may be followed by selection of informative attributes and ?nally generation of the system for the extraction of the relevant information. Machine Learning tech- niques may be employed in various stages of this process. The problem of constructing com- plex systems can thus be seen as a problem of planning to resolve multiple (possibly interacting) tasks. So, one important issue that needs to be addressed is how these multiple learning pro- cesses can be coordinated. Each task is resolved using certain ordering of operations. Meta-learning can be useful in this process. It can help us to retrieve previous solutions conceived in the past and re-use them in new settings. The aim of the workshop is to explore the possibilities of this new area, offer a forum for exchanging ideas and experience concerning the state-of-the art, permit to bring in knowledge gathered in different but related and relevant areas and outline new direc- tions for research. It is expected that the workshop will help to create a sub-community of ML / DM researchers interested to explore these new venues to ML / DM problems and help thus to advance the research and potential for new type of ML / DM systems.