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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.
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