Machine Learning in Astronomy (Iau S368): Possibilities and Pitfalls (Hardcover) (ISBN-13: 9781009345194)

Today's astronomical observatories are generating more data than ever, from surveys to deep images. Machine learning methods can be a powerful tool to harness the full potential of these new observatories, as well as large archives that have accumulated. However, users should beware of common pitfalls, including bias in data sets and overfitting. IAU Symposium 368 addresses graduate students, teachers and professional astronomers who would like to leverage machine learning to unlock these huge volumes of data. Researchers pushing the frontiers of these methods share best practices in applied machine learning. While this volume is focused on astronomy applications, the methodological insights provided are relevant to all data-rich fields. Machine learning novices and expert users will find and benefit from these fresh new insights.

Author: Jess McIver
ISBN-10: 1009345192
ISBN-13: 9781009345194
Publisher: Cambridge University Press
Language: English
Published: 10/16/2025
Pages: 200
Format: Hardcover
Weight: 0.90lbs
Size: 9.81h x 7.16w x 0.44d