  Structured light is attracting significant attention for its diverse
applications in both classical and quantum optics. The so-called vector vortex
beams display peculiar properties in both contexts due to the non-trivial
correlations between optical polarization and orbital angular momentum. Here we
demonstrate a new, flexible experimental approach to the classification of
vortex vector beams. We first describe a platform for generating arbitrary
complex vector vortex beams inspired to photonic quantum walks. We then exploit
recent machine learning methods -- namely convolutional neural networks and
principal component analysis -- to recognize and classify specific polarization
patterns. Our study demonstrates the significant advantages resulting from the
use of machine learning-based protocols for the construction and
characterization of high-dimensional resources for quantum protocols.
