Artificial Intelligence and Big Data in Astronomy credit:

4 Hours

Introduction to advanced artificial intelligence methods and their applications in astrophysics and astronomy. Topics include deep learning, generative models, uncertainty quantification, physics-informed learning, interpretable machine learning, and applications such as time series and galaxy morphological classification, deblending, photometric redshifts, and gravitational lensing inference. Students will gain hands-on experience through assignments and a final project. Prerequisite: Restricted to Graduate standing or consent of instructor.