From neill at cs.cmu.edu Thu Nov 13 18:06:14 2008 From: neill at cs.cmu.edu (Daniel B. Neill) Date: Thu, 13 Nov 2008 18:06:14 -0500 (EST) Subject: [Research] new research seminar in machine learning and policy Message-ID: Dear Auton students, Perhaps some of you might be interested in a new course that I'll be teaching in the spring (mini-4). I've attached a course description below. Please feel free to forward to any other student groups that might be interested; I've already sent out announcements to MLD and Heinz. Best, Daniel -------------------------------------------------------------------- 90-904, Research Seminar in Machine Learning and Policy Instructor: Daniel B. Neill (neill at cs.cmu.edu) 6 credits, Mini-4, Mon/Wed 10:30-11:50am The Research Seminar in Machine Learning and Policy (90-904) is a brand-new, Ph.D. level course intended for Heinz and MLD students 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: 1. Which policy problems are, and are not, amenable to application of machine learning techniques? 2. How can we integrate machine learning methodologies with core policy methods from econometrics, statistics, management science, and organizational behavior? 3. How can we use prediction methods (classification and regression) to inform policy decisions? 4. How can we use Bayesian networks and other graphical models to understand the relationships between variables in policy domains? 5. 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? 6. 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? 7. How can we channel the "wisdom of crowds" into productive tasks (e.g. games with a purpose, prediction markets). 8. How can we make machine learning systems valuable to users in real-world application domains? This course has been developed as part of a joint Heinz/MLD initiative to build closer ties between researchers and students in machine learning and policy. Another aspect of this initiative is the establishment of a new Joint Ph.D. program in Machine Learning and Public Policy (http://www.ml.cmu.edu/prospective_students/ml_heinz_phd.html). Please feel free to contact me (neill at cs.cmu.edu) if you would like more information about these courses or are interested in learning more about the joint program. From awd at cs.cmu.edu Sat Nov 15 16:41:58 2008 From: awd at cs.cmu.edu (Artur Dubrawski) Date: Sat, 15 Nov 2008 16:41:58 -0500 Subject: [Research] Lab meeting on Tuesday Nov 18th 11:30am NSH 1507 Message-ID: <491F4226.4010100@cs.cmu.edu> Hello, This Tuesday we will be treated by Purna who will reveal her newest results on fast dynamic re-ranking in large graphs. Food for flesh will be provided, too. See you all there! Artur