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
    • Parameter Estimation
    • PCA, Statistical Foundations
    • Perceptrons and Logistic Regression
    • Support Vector Machines
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Lectures

Lectures
  • Introduction
  • Nearest Neighbor Classification
  • Ellipses, Gaussians, Convariances
  • Invariances, PCA
  • PCA, Statistical Foundations
  • Classification with Normal Densities
  • Perceptrons and Logistic Regression
  • Multilayer Perceptrons
  • Support Vector Machines
  • Kernel Density Estimation
  • kNN Methods
  • Parameter Estimation



Subpages (12): 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 Perceptrons and Logistic Regression Support Vector Machines
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