Course: Pattern Recognition

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    • Classification with Normal Densities
    • Ellipses, Gaussians, Convariances
    • Introduction
    • Invariances, PCA
    • Kernel Density Estimation
    • kNN Methods
    • Multilayer Perceptrons
    • Nearest Neighbor Classification
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    • PCA, Statistical Foundations
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Lectures‎ > ‎

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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