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DTSTART:20070311T020000
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DTSTAMP:20241023T143300
DTSTART;TZID=America/Detroit:20241101T100000
DTEND;TZID=America/Detroit:20241101T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: David Hogg\, Professor of Physics and Data Science\, Center for Cosmology and Particle Physics\, Department of Physics\, New York University
DESCRIPTION:Abstract: Machine learning (ML) methods are having a huge impact across all of the sciences. However\, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. I identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. I also show that there are contexts in which the introduction of ML introduces strong\, unwanted statistical biases. My partial answers I provide (to the question in my title) come from the particular perspective of physics.\n\nWork in collaboration with Soledad Villar at JHU.\n\nhttps://cosmo.nyu.edu/hogg/
UID:124546-21853173@events.umich.edu
URL:https://events.umich.edu/event/124546
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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