Tobias Golling (Universite de Geneve (CH))
ML is an established tool in HEP and there are many examples which demonstrate its importance for the kind of classification and regression problem we have in our field. However, there is also a big potential for future applications in yet untapped areas. I will summarise these opportunities and highlight recent, ongoing and planned studies of novel ML applications in HEP. Certain aspects of the problems we are faced with in HEP are quite unique and represent interesting benchmark problems for the ML community as a whole. Hence, efficient communication and close interaction between the ML and HEP community is expected to lead to promising cross-fertilisation. This talk attempts to serve as a starting point for such a prospective collaboration.