CAREER: Using Machine Learning to Understand and Enhance Human Learning Capacity

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Understanding and enhancing human learning are important challenges in the 21st century. Existing human category learning models cannot quantify important capacities such as people's (in)ability to generalize from training to test, to learn from imperfect data, or to learn by actively asking questions. This research project studies human learning using machine learning. It first develops machine learning theory and algorithms to quantify these human learning capacities: It establishes learning-theoretic error bounds on human generalization performance; It models human learning from an imperfect teacher with non-parametric Bayesian methods; It models human's ability to ask informative questions with active learning theory. The project then studies computational approaches to enhance human learning: It develops "machine teaching" algorithms when the computer knows the target concept, and selects the optimal training examples to teach a human learner; It develops "human machine co-learning" algorithms when the computer does not know the target concept, but instead learns alongside the human and suggests better learning strategies to her. Each topic is verified by human experiments.The project advances machine learning with new learning theory and algorithms on tasks where humans excel. It advances cognitive psychology with new models of human learning. It has broader impacts in understanding human intelligence, and in benefiting students with new educational tools. This research project is integrated with an educational plan that incorporates undergraduate and graduate teaching and mentoring, developing a new course and a book on machine and human learning, organizing seminars, tutorials and workshops, and sharing all results on a website.