Machine Learning in Astronomy

by Dr Emille Ishida (LPC-UCA)


The availability of large data sets revolutionized many areas of scientific research, astronomy included. The current -- and in many ways already overwhelming -- data paradigm will suffer still another revolution with the advent of the new generation of large astronomical surveys. In this new scenario, the use of automated methods of analysis will be unavoidable. In this talk, I will give a short introduction to the basic principles of machine learning  and describe situations where they are traditionally used in astronomical research. I will also present how domain knowledge can be used to optimize results from traditional algorithms by incorporating expert feedback in the learning process.