Pattern recognition deals with automated classification, identification, and/or characterizations of signals/data from various sources. The main objectives of this graduate module are to equip students with knowledge of common statistical pattern recognition (PR) algorithms and techniques. Course will contain project-based work involving use of PR algorithms. Upon completion of this module, students will be able to analyze a given pattern recognition problem, and determine which standard technique is applicable, or be able to modify existing algorithms to engineer new algorithms to solve the problem. Topics covered include: Decision theory, Parameter estimation, Density estimation, Non-parametric techniques, Supervised learning, Dimensionality reduction, Linear discriminant functions, Clustering, Unsupervised learning, Feature extraction and Applications.