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[Colloquium] Complex causal learning

November 11, 2011

Watch Colloquium: 

M4V file (620 MB)

  • Date: Friday, November 11, 2011 
  • Time: 12:00 pm — 12:50 pm
  • Place: Centennial Engineering Center 1041

David Danks
 Mellon University

In the past twenty years, multiple machine learning algorithms have been developed that learn causal structure from observational or experimental data. Most of the algorithms were designed, however, for relatively “clean” data from linear systems, and so are often not applicable to real-world problems. In this talk, I will first outline the principles underlying this type of causal learning, and then examine three new algorithms developed for more complex causal learning: specifically, for non-linear and/or non-Gaussian data, and for learning from multiple, overlapping datasets. Time permitting, I will provide case studies (e.g., from oceanography) showing these algorithms in action.

 

Bio: David Danks is an Associate Professor of Philosophy & Psychology at Carnegie Mellon University, and a Research Scientist at the Institute for Human & Machine Cognition. His research focuses on the interface of cognitive science and machine learning: using the tools of machine learning to better understand complex human cognition, and developing novel machine learning algorithms based on human cognitive capacities. His research has centered on causal learning and reasoning, category development and application, and decision-making.