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Course Details

Course Number: 90-904

Research Seminar in Machine Learning and Policy

Units: 12

This research seminar is intended for Ph.D. students in Heinz College, the Machine Learning Department, and other university departments who wish to engage in cutting-edge research at the intersection of machine learning and public policy. Qualified master's students may also enroll with permission of the instructor; all students are expected to have some prior background in machine learning and/or artificial intelligence (10-601, 10-701, 90-866, or a similar course).

The course has three main objectives: 1) to facilitate in-depth discussions of current research articles and essential topics in machine learning and policy, 2) to benefit the students' own ongoing research projects through presentations, critiques, and discussions, and 3) to encourage interdisciplinary research collaborations between students in Heinz, MLD, and other departments. We plan to achieve these goals through a discussion-based course format: students will present and discuss current research articles on selected topics in machine learning and policy, as well as giving presentations on their ongoing research projects and/or smaller-scale course projects in this domain.

This course is meant to provide in-depth coverage of selected topics in machine learning and policy. While the set of discussion topics will vary from semester to semester, examples include:

Which policy problems are, and are not, amenable to application of machine learning techniques?
How can we integrate machine learning methodologies with core policy methods from econometrics, statistics, management science, and organizational behavior?
How can we use prediction methods (classification and regression) to inform policy decisions?
How can we use Bayesian networks and other graphical models to understand the relationships between variables in policy domains?
How can we combine machine learning and operations research methodologies to find patterns in massive datasets? How can such data mining techniques be used for the public good without violating individual privacy?
How can we integrate approaches to modeling and mining of social network data with a policy-based understanding of the formation and evolution of social ties?
How can we channel the "wisdom of crowds" into productive tasks (e.g. games with a purpose, prediction markets).
How can we make machine learning systems valuable to users in real-world application domains?

Faculty:
Daniel B. Neill