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