This project aims to provide a foundation for applying machine learning in practice. The research component has two goals. Firstly, to develop data collection and refinement methods that allow machine learning algorithm to extract structure from data. Secondly, to perform empirical and theoretical analyses of machine learning algorithms to develop a deeper understanding of current and new methods, and to uncover the characteristics of datasets that make them amenable to particular learning algorithms. The education component of this project aims to provide practitioners with the foundation required to apply machine learning successfully in practice by providing a deeper understanding of the type of structure that various learning algorithms can detect, by creating tools for collecting data, by giving students hands on experience applying machine learning in practice, and through inter-disciplinary research projects. These research and education activities are being performed in the context of several application domains, including mapping global land cover of the Earth from remotely sensed data, content-based image retrieval of high resolution CAT scans of the lung, learning to detect anomalies for computer security, and on-board processing of planetary data. In addition to its contribution to applied machine learning, this project has the benefit that the successful application of machine learning to proposed application domains will result in non-trivial contributions to medicine, computer systems development, and geography. http://mow.ecn.purdue.edu/ brodley/
