Speaker
Description
In the era of astronomical "big data", with the amount of observational
data ever-increasing and about to increase by orders of magnitudes
during the next decade, machine learning has become not only a commodity
but also a necessity. At the same time, the application of machine
learning methods to astrophysical problems yields many specific
challenges. One of them is related to the fact that while the data to
which we want to apply these methods are often big, the available
training samples are small. Moreover, they are often not really
representative, in a way that may be difficult to quantify, which faces
us with a variety of extrapolation problems. More challenges are related
to the interpretability of the results, given the limited information we
can access. I will try to discuss the aims, difficulties and attempts to
overcome them, making use, among other things, of examples from the
research made in our extragalactic astrophysics group in NCBJ and UJ.
