Introduction to pattern recognition, feature extraction, and classification. Bayesian
decision theory, maximum likelihood and Bayesian parameter estimation, Nonparametric
pattern classification techniques, density estimation. Pattern Classification using linear
discriminant functions. Unsupervised machine learning, clustering, vector quantization,
K-means. Various methods of pattern recognition, extraction methods, statistical
classification, various classifiers and case studies.​