Machine Learning in Astronomy (IAU S368). Possibilities and Pitfalls, Hardback

IAU S368 addresses graduate students and professional astronomers who wish to leverage machine learning to unlock the potential of modern data-rich surveys and deep images, as well as archival data. Researchers at the frontiers share best practices in applied machine learning that are relevant to astronomy and other data-rich fields.

• Publisher: Cambridge University Press
• Illustration(s): Worked examples or Exercises
• Number of pages: 200
• Collection: Proceedings of the International Astronomical Union Symposia and Colloquia
• Publication date: 2025
• Dimensions: 178 x 254 x 11
• Cover type: Hardback
• Editor(s): Christopher (Swinburne University of Technology, Victoria) Fluke