Machine Learning in Astronomy (Iau S368): Possibilities and Pitfalls - Hardcover
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Availability:In StockContributor:Jess McIver (Editor), Ashish Mahabal (Editor), Christopher Fluke (Editor)Series:Proceedings of the International Astronomical Union SymposiaPublish date:10/16/2025Pages:200
Language:EnglishPublisher:Cambridge University PressISBN-13:9781009345194ISBN-10:1009345192UPC:9781009345194Book Category:ScienceBook Subcategory:Space ScienceBook Topic:AstronomySize:9.81 x 7.16 x 0.44 inchesWeight:0.9017Product ID:SCEYW0VQH2
Machine Learning in Astronomy (Iau S368): Possibilities and Pitfalls
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,...
Series: Proceedings of the International Astronomical Union Symposia
Language:EnglishPublisher:Cambridge University PressISBN-13:9781009345194ISBN-10:1009345192UPC:9781009345194Book Category:ScienceBook Subcategory:Space ScienceBook Topic:AstronomySize:9.81 x 7.16 x 0.44 inchesWeight:0.9017Product ID:SCEYW0VQH2
Publisher: Cambridge University Press
Contributor(s)
Jess McIver (Editor), Ashish Mahabal (Editor), Christopher Fluke (Editor)
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