  In this article, we continue our study on universal learning machine by
introducing new tools. We first discuss boolean function and boolean circuit,
and we establish one set of tools, namely, fitting extremum and proper sampling
set. We proved the fundamental relationship between proper sampling set and
complexity of boolean circuit. Armed with this set of tools, we then introduce
much more effective learning strategies. We show that with such learning
strategies and learning dynamics, universal learning can be achieved, and
requires much less data.
