The goal of the workshop was to explore research opportunities in the broad domain of the Science and Engineering of Learning, and to provide NSF with this Report identifying important open questions. It is anticipated that this Report will to be used to encourage new research directions, particularly in the context of the NSF Science of Learning Centers (SLCs), and also to spur new technological developments. The workshop was attended by 20 leading international researchers. Half of the researchers at the workshops were from SLCs and the other half were experts in neuromorphic engineering and machine learning. The format of the meeting was designed to encourage open discussion. There were only relatively brief formal presentations. The most important outcome was a detailed set of open questions in the domains of both biological learning and machine learning. We also identified set of common issues indicating that there is a growing convergence between these two previously separate domains so that work invested there will benefit our understanding of learning in both man and machine. In this summary we outline a few of these important questions. Biological learners have the ability to learn autonomously, in an ever changing and uncertain world. This property includes the ability to generate their own supervision, select the most informative training samples, produce their own loss function, and evaluate their own performance. More importantly, it appears that biological learners can effectively produce appropriate internal representations for composable percepts-a kind of organizational scaffold-as part of the learning process. By contrast, virtually all current approaches to machine learning 2 typically require a human supervisor to design the learning architecture, select the training examples, design the form of the representation of the training examples, choose the learning algorithm, set the learning parameters, decide when to stop learning, and choose the way in which the performance of the learning algorithm is evaluated. This strong dependence on human supervision is greatly retarding the development and ubiquitous deployment autonomous artificial learning systems. Although we are beginning to understand some of the learning systems used by brains, many aspects of autonomous learning have not yet been identified. The mechanisms of learning operate on different time scales, from milliseconds to years. These various mechanisms must be identified and characterized. These time scales have practical importance for education. For example, the most effective learning occurs when practice is distributed over time such that learning experiences are separated …