Syllabus

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 discriminant functions. Fisher's linear discriminant and linear perceptron. Kernel methods and Support vector machine. Decision trees for classification. Unsupervised learning and clustering. K ‐ means and hierarchical clustering. Decision Trees for classification. Ensemble/ Adaboost classifier, Soft computing paradigms for classification and clustering. Applications to document analysis and recognition