  We propose a novel approach to distinguish table vs non-table ideals by using
different machine learning algorithms. We introduce the reader to table ideals,
assuming some knowledge on commutative algebra and describe their main
properties. We create a data set containing table and non-table ideals, and we
use a feedforward neural network model, a decision tree and a graph neural
networks for the classification. Our results indicate that there exists an
algorithm to distinguish table ideals from non-table ideals, and we prove it
along some novel results on table ideals.
