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 a diverse and exciting field, and there ar e multiple ways of defining it: 1. The Artifical Intelligence View. Learning is central to human knowledge and intelligence, and, likewise, it is also essential for building intelligent machines. Years of effort in AI

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