  A great deal of attention has been recently given to Machine Learning (ML)
techniques in many different application fields. This paper provides a vision
of what ML can do in Power Line Communications (PLC). We firstly and briefly
describe classical formulations of ML, and distinguish deterministic from
statistical learning models with relevance to communications. We then discuss
ML applications in PLC for each layer, namely, for characterization and
modeling, for the development of physical layer algorithms, for media access
control and networking. Finally, other applications of PLC that can benefit
from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical
examples are reported to serve the purpose of validating the ideas and motivate
future research endeavors in this stimulating signal/data processing field.
