This course is designed as an introduction to the techniques and involved in the measurement and analysis of astronomical phenomena. The methods of analysis are broadly applicable to other areas of science. The activities are designed to emphasize the entire life cycle of experiments in astrophysics, from design and proposal to analysis and communication of the results. Lectures will be used to supplement the laboratory activities and introduce new topics. By the end of course, you will be able to:
Project-based course designed for students with no previous experience in machine learning to learn about methods and algorithms in machine learning and their application to scientific problems in physical sciences. Development of experience in compilation, analysis, and cleaning of data. Machine learning topics include classification, regression, dimensionality reduction, clustering, and kernel methods.
Learning Outcomes:
Statistical Mechanics is the application of classical and quantum mechanics to large systems in order to derive the macroscopic behavior of matter. The term statistical mechanics comes from the fact that large systems cannot be solved exactly and we must instead deal with statistical behavior without concern about individual particles. Large systems also exhibit behavior not found for individual particles and we will deal with new macroscopic quantities such as temperature and entropy. Since astronomy deals with very large systems, only through statistical mechanics and its offshoot thermodynamics can we understand stellar interiors, planetary atmospheres, interstellar gas, and a slew of other astrophysical environments. At the same time, astrophysical observations of objects such as white dwarfs and neutron stars feed back into our understanding of fundamental physics.
One effective way of teaching science is through the use of inquiry-based learning. A good summary of inquiry is provided by the Exploratorium:
"Inquiry is an approach to learning that involves a process of exploring the natural or material world, that leads to asking questions and making discoveries in the search for new understandings. Inquiry, as it relates to science education, should mirror as closely as possible the enterprise of doing real science."
I'm interested in designing and implementing science inquiry activities to teach science at the high school, undergraduate, and graduate level. I have designed activities for the CfAO Adaptive Optics summer school and the short course for preparing undergraduates for research at the University of Toronto.
An overview of the laboratory activities developed for the CfAO Summer School as well as the Fourier Optics inquiry activity are presented in a conference proceeding Learning from Inquiry In Practice:
I was a part of the organizing committee for the Summer Undergraduate Research Program (SURP) at the University of Toronto in 2013 and 2014. This program brings together about two dozen students every summer for research experience at either the Dunlap Institute, Department of Astronomy & Astrophysics (DAA), or the Canadian Institute for Theoretical Astrophysics (CITA). The goals of SURP are to: introduce undergraduates to research; improve science writing and communication skills; work with faculty/postdocs on a research project; and provide information for career decisions.