Today, astronomical observatories produce more data than ever before, 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 the large archives that have been accumulated. However, users should be aware of common pitfalls, such as data set bias and overfitting.
IAU Symposium 368 is addressed to postgraduate students, teachers, and professional astronomers who wish to leverage machine learning to unlock these massive amounts of data. Researchers advancing the boundaries of these methods share best practices in applied machine learning. While this volume focuses on applications in astronomy, the methodological information provided is relevant across all data-rich fields.
Novices in machine learning and experienced users will find and benefit from these fresh new insights.
Pages: 200, Dimensions: 17.8x17.8cm
