Cost-sensitive learning aims to minimize the data acquisition cost while maximizing the accuracy of the learner/predictor. Many sub-fields in machine learning such as semi-supervised learning, active label/feature acquisition, cascaded classification, and inductive transfer are motivated by the need to minimize the cost of data acquisition in various application domains. These approaches typically attempt to minimize data acquisition costs under strong simplifying assumptions -- e.g., features vectors are assumed to have zero cost in semi-supervised learning. Although all of these areas have felt the need for a principled solution to minimize data costs, until recently the acquisition cost has rarely been modeled directly. Despite some recent work in this area, much more research is needed on this important topic. It is also important to ensure that the theoretical work addresses the practical needs of several application communities such as computer aided medical diagnosis, signal processing, remote sensing, computer vision, etc. We hope to bring together researchers from semi-supervised learning, active label/feature acquisition, inductive transfer learning, cascaded classification and other theoretical areas with practitioners from various application domains. We welcome both novel theory/algorithms and contributions that draw attention to open problems and challenges in real-world applications which call for cost-sensitive learning.