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SUMMARY:Effective Use of Machine Learning in Astrophysics - Miles Cranmer 
 (Institute of Astronomy)
DTSTART:20231124T113000Z
DTEND:20231124T123000Z
UID:TALK207310@talks.cam.ac.uk
CONTACT:Steven Brereton
DESCRIPTION:The field of machine learning (ML) offers a powerful set of fr
 ameworks for addressing complex problems in astrophysics\, ranging from em
 ulating expensive simulations to performing anomaly detection in large dat
 asets. This talk explores a diverse range of ML applications within astrop
 hysics\, highlighting the role of these methods in extracting insights fro
 m multidimensional and multimodal datasets. I will also discuss the major 
 challenges of ML\, such as model robustness\, interpretability\, uncertain
 ty estimation\, and incorporation of physical priors. In all\, this presen
 tation will provide astronomers with a pragmatic overview of machine learn
 ing's capabilities and limitations\, and how these techniques will continu
 e to shape astrophysical discovery.
LOCATION:Hoyle Lecture Theatre and online (details to be sent by e-mail)
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