Pattern Recognition
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A C T I V I T I E S

Syllabus

      Pattern Recognition Systems

       Basic Structure of Pattern Recognition Systems 

       Design of Pattern Recognition Systems 

       Supervised and Unsupervised Learning and Classification 

      Bayesian Decision Theory and Optimal Classifiers

      Discriminant Functions and Decision Surfaces

      Supervised Learning of the Bayes Classifier

       Parametric Estimation

       Non-Parametric Estimation of Density Functions

       Parzen Windows

       k-Nearest Neighbors Classifier

       Linear Discriminant Functions and Classifiers

       Classifier Evaluation

      Unsupervised Learning and Clustering

       K-means Clustering

       K-means

       K-means Algorithm

       Properties of the K-means

       Finite Mixture

       EM Algorithm

      Neural Networks

       Perceptron Criterion and Algorithm in 2-Class Case

       Perceptron Criterion 

       Perceptron Algorithm

       Back-propagation Neural Networks