This dissertation explores both the algorithmic and statistical aspects of active learning for binary classification. What are effective procedures for determining which data to label? How can these procedures take advantage of the interactive learning process, and in what circumstances do they yield improved learning performance compared to standard passive learners? To answer these questions, we develop and rigorously analyze a broad class of general active learning methods that address the essential algorithmic and statistical difficulties of the problem. Algorithms for Active Learning