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.

Same as Reference

1. Pattern Classification (Pt.1) 2nd Edition by Richard O. Duda, Peter E. Hart, David G. Stork

2. “Pattern Recognition and Machine Learning”, C.M. Bishop, 2nd Edition, Springer, 2011.

3. Sergios Theodoridis, "Machine Learning: A Bayesian and Optimization Perspective". Elsevier, 2015.