  Machine Learning software documentation is different from most of the
documentations that were studied in software engineering research. Often, the
users of these documentations are not software experts. The increasing interest
in using data science and in particular, machine learning in different fields
attracted scientists and engineers with various levels of knowledge about
programming and software engineering. Our ultimate goal is automated generation
and adaptation of machine learning software documents for users with different
levels of expertise. We are interested in understanding the nature and triggers
of the problems and the impact of the users' levels of expertise in the process
of documentation evolution. We will investigate the Stack Overflow Q/As and
classify the documentation related Q/As within the machine learning domain to
understand the types and triggers of the problems as well as the potential
change requests to the documentation. We intend to use the results for building
on top of the state of the art techniques for automatic documentation
generation and extending on the adoption, summarization, and explanation of
software functionalities.
