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Course Introduction Course Statistical Pattern Recognition Course code S204E106 Teacher’s name Pang Yanwei Title Professor School Electronic Information Engineering Semester Spring Hours 32 Credit 2 Teaching method Teaching 24 hours, Discussion 8 hours Course Introduction Pattern recognition plays an important role in computer vision, speech recognition, artificial intelligence, data mining, etc. It is also widely used in the field of information and communication. Statistical method dominates the pattern recognition. The main content of the course includes Bayesian decision theory, probability density estimation, subspace analysis, tensor analysis, clustering analysis, feature selection, and classifier fusion, etc. Moreover, state-of-the-art methods of statistical pattern recognition are to be introduced. Armed with the above methods and theory, how to apply statistical pattern recognition to face recognition, intelligence visual surveillance, and human-machine interaction will also be described. Examination: Academic report Teaching Materials 1. Pattern Classification, Second Edition, R. Duda, P.E. Hart, D.G. Stork, Mechanical Industrial Press, 2004. 2. Bibligraphy 1.Pattern Recognition And Machine Learning, Christopher M. Bishop, Springer, 2007. 2. 3.