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Machine Learning and Data Mining Lecture Notes

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Machine Learning and Data MiningLecture NotesCSC 411/D11Computer Science DepartmentUniversity of TorontoVersion: February 6, 2012Copyrightc 2010 Aaron Hertzmann and David FleetCSC 411 / CSC D11CONTENTSContentsConventions and Notationiv1Introduction to Machine of Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . simple problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..22Linear 1D case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . inputs . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . outputs.

Machine learning is the marriage of computer science and statistics: com-putational techniques are applied to statistical problems. Machine learning has been applied to a vast number of problems in many contexts, beyond the typical statistics problems. Ma-chine learning is often designed with different considerations than statistics (e.g., speed is

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