  Though being seemingly disparate and with relatively new intersection, high
energy nuclear physics and machine learning have already begun to merge and
yield interesting results during the last few years. It's worthy to raise the
profile of utilizing this novel mindset from machine learning in high energy
nuclear physics, to help more interested readers see the breadth of activities
around this intersection. The aim of this mini-review is to introduce to the
community the current status and report an overview of applying machine
learning for high energy nuclear physics, to present from different aspects and
examples how scientific questions involved in high energy nuclear physics can
be tackled using machine learning.
