  Machine learning presents a general, systematic framework for the generation
of formal theoretical models for physical description and prediction.
Tentatively standard linear modeling techniques are reviewed; followed by a
brief discussion of generalizations to deep forward networks for approximating
nonlinear phenomena and universal computers.
