Inverse problems (IP) of indirect measurements
are a class of IP encountered in most modern nature science
experiments. Unfortunately, they are characterized by a
number of properties making them hard to solve: they may be
ill-posed or even incorrect, non-linear, and often they are
characterized by high dimension by input and/or by output. As
such, IP of indirect measurements require special methods to
solve them. One of the classes of such methods are methods of
machine learning (ML), which however possess special
properties which should be taken into account when using
them. In this paper, the authors suggest an outline of a special
methodology, which can become the base for a standard
scenario for processing data of indirect measurement IP with
ML methods. The main notions underlying this methodology
are also described and explained
