ASTRO 4523

Modeling, Mining and Machine Learning in Astronomy

Course Description

Course information provided by the 2025-2026 Catalog.

This course builds upon a review of probability and statistics, and basic signal processing principles to explore, develop, and apply algorithms for discovering objects and events in astronomical data, for inference of sophisticated models for populations of objects using frequentist and Bayesian methods, and for visualization and presentation of results to address fundamental questions using persuasive, data-based arguments. Methods include time-series analysis; clustering, classification algorithms, genetic and Markov Chain Monte Carlo algorithms, and neural networks with different architectures. Examples using simulated and actual data will be python based, including Jupyter notebooks.

Prerequisites REF-FA25/Corequisites REF-FA25 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. Corequisites: None.

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

Last 4 terms offered (None)

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