The pattern recognition course describes various methods and techniques that are used in pattern recognition. Topics include Bayes decision theory, description of patterns, feature extraction and classification, classification models, non-parametric pattern classification techniques, parameter estimation, pattern classification using linear discriminant functions, uncertainty in pattern recognition, fuzzy sets, perception algorithms and its extensions, learning of rules for pattern recognition, learning discriminates, unsupervised learning and clustering algorithms, feature extraction and algorithms, neural network techniques including Hopfield, feed forward model, and training of neural networks, structural recognition techniques, and other pattern recognition methods and applications.