Machine learning in stellar physics

I will try to┬ápresent, in a heavily biased way, a broader picture of what machine learning is and how it can help us in extracting new knowledge from astronomical data. The approach of using ML techniques has become important in recent times as we welcome the flood of big data in almost every field of research, but it is important to know which methods work well and for which purpose, so I will describe some basics and present some applications of both unsupervised and supervised techniques applied to selected astronomical subjects. At the end we’ll also have a look into an interesting and novel approach of data augmentation with GAN and how it could help in analysis of stellar spectra.