  Machine learning (ML) is quickly emerging as a powerful tool with diverse
applications across an extremely broad spectrum of disciplines and commercial
endeavors. Typically, ML is used as a black box that provides little
illuminating rationalization of its output. In the current work, we aim to
better understand the generic intuition underlying unsupervised ML with a focus
on physical systems. The systems that are studied here as test cases comprise
of six different 2-dimensional (2-D) particulate systems of different
complexities. It is noted that the findings of this study are generic to any
unsupervised ML problem and are not restricted to materials systems alone.
Three rudimentary unsupervised ML techniques are employed on the adjacency
(connectivity) matrix of the six studied systems: (i) using principal
eigenvalue and eigenvectors of the adjacency matrix, (ii) spectral
decomposition, and (iii) a Potts model based community detection technique in
which a modularity function is maximized. We demonstrate that, while solving a
completely classical problem, ML technique produces features that are
distinctly connected to quantum mechanical solutions. Dissecting these features
help us to understand the deep connection between the classical non-linear
world and the quantum mechanical linear world through the kaleidoscope of ML
technique, which might have far reaching consequences both in the arena of
physical sciences and ML.
