University of Toronto
While many machine learning (ML) applications are often rooted in statistics or particular domain applications (e.g., image processing), applications in astronomy often tend to leave behind a lot of the types of thinking involved with designing traditional astronomical or statistical models. In this somewhat informal talk, I will try to highlight a number of examples where "astrostatistical thinking" from myself and others has proven fruitful in tackling problems in several areas. Topics may include (based on audience interest): incorporating various uncertainties into ML models, dealing with outliers in training and prediction, designing robust ML workflows, vetting and interpreting ML-driven results, "calibrating" posterior uncertainties, and trying to select between various high-dimensional non-parametric ML models (based on audience interest). Applications for these methods range from photometric redshift estimation, 3-D dust mapping, chemical tagging, stellar age estimation, and more.
|Date: ||Jeudi, le 12 octobre 2023|
|Lieu: ||Université de Montréal|
| ||Pavillon MIL A-3561|