CMP 265: Pattern Recognition Methods and Applications  (Graduate Course, Second Semester)

OBJECTIVES: This course introduces the fundamentals of statistical pattern recognition with examples from several application areas. Techniques for handling multidimensional data of various types and scales along with algorithms for clustering and classifying data are explained. This is a graduate level course suited for graduate students in Computer Science and Engineering. It is primarily intended for highly motivated graduate students who are interested in doing research in the areas of Pattern Recognition, Artificial Intelligence, Image Processing, Graphics and Computer Vision. Many open problems in these areas suitable for investigation by Master's or Ph.D. students will be presented and proposed.

COURSE OUTLINE:

·  Introduction to Pattern Recognition

·  Bayesian Decision Theory

·  Maximum Likelihood and Bayesian Estimation

·  Non-Parametric Techniques and Linear Discriminant Functions

·  Stochastic Methods  and Pattern Recognition (Selected Topics)

·  Graphic (Graph-Based) Models in Pattern Recognition

·  Clustering (Selected Topics)

BIBLIOGRAPHY:

·         R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience.         2001

Complementary

·         K. Fukunaga, Statistical Pattern Recognition, 2nd edition, Morgan Kaufmann, 1990

·         T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer-Verlag, 2009.

·         Selected papers (e.g.: IEEE PAMI, CVPR, etc.)

Hours/Week: 4

Credits: 4