Machine Learning

Research in machine learning at Michigan encompasses reinforcement, unsupervised, and supervised learning. In reinforcement learning, we focus on building autonomous agents that can learn to act in complex, sequential, and uncertain environments. In particular, a number of research projects derive from an interest in building long-lived and flexibly-competent agents rather than the more usual agents that perform one complex task repeatedly. In unsupervised learning, we focus on developing methods for automatically constructing deep and hierarchical feature representations of high-dimensional data with applications to computer vision and more generally to sensory information processing and perception. Other areas of interest include: 1) the integration of multiple learning methods into the cognitive architecture Soar; 2) developing specialized reinforcement learning methods for behavior-change and treatment-design in healthcare settings ; 3) developing specialized methods for learning in large-scale games and other multiagent problems; and 4) developing unsupervised, semi-supervised, and supervised learning algorithms for information retrieval and natural language processing as well as for computational biomarkers in medical domains.

CSE Faculty

Baveja, Satinder Singh
Fouhey, David
Hayes, John P.
Johnson, Justin
Koutra, Danai
Kuipers, Benjamin
Laird, John E.
Lee, Honglak
Mihalcea, Rada
Mower Provost, Emily
Olson, Edwin
Wiens, Jenna

ECE Faculty

Balzano, Laura
Berenson, Dmitry
Fessler, Jeffrey A.
Hero, Alfred O.
Scott, Clayton D

Related Labs, Centers, and Groups
Center for the Study of Complex Systems
Vision & Learning Lab