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Classification with Normal Densities
Ellipses, Gaussians, Convariances
Introduction
Invariances, PCA
Kernel Density Estimation
kNN Methods
Multilayer Perceptrons
Nearest Neighbor Classification
Parameter Estimation
PCA, Statistical Foundations
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Classification with Normal Densities
Lecture 6
2009-11-30
Lecture & Reading
patrec_-_Central_Limit_Theorem_--_Sage.pdf
patrec_-_Decision_Regions_for_the_Normal_Density_--_Sage.pdf
patrec_-_Normal_Generative_Model_--_Sage.pdf
Homework
Homework 6
Lecture 7
2009-12-07
Lecture & Reading
patrec_-_Decision_Regions_for_the_Normal_Density_--_Sage.pdf
patrec_-_MNIST_-_k-means_--_Sage.pdf
patrec_-_MNIST_-_k-means_evaluation_--_Sage.pdf
patrec_-_MNIST_-_online_k-means_--_Sage.pdf
patrec_-_EM_and_Gaussian_Mixture_Modeling_--_Sage.pdf
patrec_-_MNIST_-_SOM_--_Sage.pdf
patrec_-_MNIST_-_SOM_torus_--_Sage.pdf
Homework
Homework 7
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