ASTRO 4523

Modeling, Mining and Machine Learning in Astronomy

Course Description

Course information provided by the 2025-2026 Catalog.

This course covers probability, statistics, and signal processing to develop algorithms for detecting objects and events in astronomical data. Topics include frequentist and Bayesian model inference, time-series analysis, clustering, classification, genetic algorithms, Markov Chain Monte Carlo, and neural networks. Students will apply these methods to real and simulated data using Python and Jupiter notebooks.

Prerequisites background in probability and statistics at the level of ENGRD 2700 or MATH 1710 or equivalent; lower division math background equivalent for a physics or engineering major.

Enrollment Information Recommended prerequisite: knowledge of Python or MATLAB.

Distribution Requirements (OPHLS-AG), (PHS-AS, SDS-AS)

Last 4 Terms Offered 2025FA, 2023SP, 2021SP, 2019SP

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