PR overview-Feature extraction-Statistical Pattern Recognition-Supervised Learning-Parametric
methods-Non parametric methods; ML estimation-Bayes estimation-k NN
approaches. Dimensionality reduction, data normalization. Regression, and time series
analysis. Linear discriminat functions. Fishers linear discriminant, linear perceptron and Neural
Networks. Kernel methods and Support vector machine. Unsupervised learning and clustering.
K-means and hierarchical clustering. Ensemble/ Adaboost classifier, Soft computing paradigms
for classification and clustering. Applications to document analysis and recognition