In these proceedings we perform a brief review of machine learning (ML)
applications in theoretical High Energy Physics (HEP-TH). We start the
discussion by defining and then classifying machine learning tasks in
theoretical HEP. We then discuss some of the most popular and recent published
approaches with focus on a relevant case study topic: the determination of
parton distribution functions (PDFs) and related tools. Finally, we provide an
outlook about future applications and developments due to the synergy between
ML and HEP-TH.Comment: 7 pages, 3 figures, in proceedings of the 18th International Workshop
on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017
