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  2. ASTRON 205

Machine Learning for Astrophysicists
ASTRON 205

Course Information

Description

A survey course of statistical and data-driven methodologies widely utilized in astrophysics. Topics will include introductory Bayesian statistics, unsupervised methods (dimensionality reduction, density estimation, clustering), supervised methods (classification, prediction, inference), and deep learning techniques (neural networks, generative methods). This course will focus on both the theory behind these methodologies and their application to astronomical datasets. This course is open to undergraduate and graduate students.

School Faculty of Arts & Sciences
Credits 4
Cross Reg

N/A

Department Astronomy
Course Component Lecture
Subject Astronomy
Grading Basis FAS Letter Graded
Course Requirements Course open to Graduate Students Only (Undergraduates can submit a request to enroll)
General Education N/A
Quantitative Reasoning with Data N/A
Divisional Distribution Science & Engineering & Applied Science
Course Level Primarily for Graduate Students