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: Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal 91ÃÛÌÒ¸ó of statistical learning theory. Analyzes some important classes of machine learning methods. Specific topics may include the PAC framework, VC-dimension and Rademacher complexity.

Requires prerequisite course of APPM 4440 "Real Analysis" (minimum grade C-).

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