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Machine Learning and Data Mining Lecture Notes

Machine Learning and Data MiningLecture NotesCSC 411/D11 Computer Science DepartmentUniversity of TorontoVersion: February 6, 2012 Copyrightc 2010 Aaron Hertzmann and David FleetCSC 411 / CSC D11 CONTENTSC ontentsConventions and Notationiv1 Introduction to Machine of Machine Learning .. simple problem ..22 Linear 1D case .. inputs .. outputs ..83 Nonlinear function regression .. and Regularization .. Neural Networks .. Neighbors .. a quadratic .. 185 Basic Probability logic .. definitions and rules .. random variables .. and Multinomial distributions .. expectation .. 266 Probability Density Functions (PDFs) expectation, mean, and variance.

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-

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