This course provides an introduction to the important concepts, theories and algorithms of pattern recognition. The topics cover Bayesian decision theory, maximum likelihood and Bayesian parameter estimation, support vector machine, boosting, nonparametric pattern recognition methods, and clustering. It also includes applications of pattern recognition in different fields. Students taking this course are expected to have the background knowledge of calculus, linear algebra, probability and random process as a prerequisite. Pre-requisite: ELEG3410 or with the consent of the instructor. (For ELEG major and minor undergraduate as elective course; For students in MPhil-PhD programmes under Faculty of Engineering; Prerequisite: ELEG 3410 or with the consent of the instructor; Not for students who have taken ELEG5503 or ELEG5410.)