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 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.