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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 .. distributions .. distributions .. Gaussian distribution .. a binomial distribution .. Rule .. estimation.

2 Linear Regression 5 ... 17 Support Vector Machines 115 ... single “silver bullet” for all learning. Using machine lear ning in practice requires that you make use of your own prior knowledge and experimentation to solve problems. But with the tools of machine learning, you can do amazing things! ...

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