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

Induction and physical theory formation as well as universal computation by machine learning