The course presents a description of the general pattern recognition problem and the general methods employed for basic pattern recognition applications. Bayes theory is presented as the building block for statistical pattern recognition methods along with the different approaches used for solving real world problems. The techniques presented include both supervised and unsupervised methods and feature selection and reduction techniques.