An Introduction to Active Learning David Cohn, Just Research Traditionally, most machine learning research has treated learning as a passive phenomenon, and has treated the learner as a passive recipient of data to be processed. This approach ignores the fact that, in many situations, the learner's most powerful tool is its ability to act, to gather data, and to influence the world it is trying to understand. Active learning is the study of how to use this ability effectively. I will review the general framework of active learning and give illustrative examples from several applications. I will show how, with suitable abuse of statistics, one can compute the "optimal" way to do active learning on a variety of machine learning architectures. As time permits, I will also describe my recent work on "active learning on a fixed budget" and discuss preliminary results and open problems surrounding it.