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Pattern Recognition: Theory and Applications


The principal purpose of this course is to introduce the student to the problems of pattern recognition through a comparative presentation of methodology and practical examples. The course is intended for computer science students with an applied mathematics orientation, and also for students in other programs (computer and electrical engineering, statistics, mathematics, psychology) who are interested in this area of research. It is also recommended for students who plan to work in the area of biomedical applications of computers.

B (M.S.)
B (Ph.D.)

Cognition and recognition: pattern recognition as an inductive process. Similarity measures and probability. Statistical classification: recognition as a decision problem. Linear and nonlinear discriminants. Artificial neural networks (ANNs). Sequential methods in classification. Tree classifiers and induction from rules. Support vector machines. Kernel function methods. Error estimation methods; bootstrapping and cross-validation. Feature extraction methods. Applications in image and character recognition; signal recognition; and automated medical diagnosis.

Expected Work: 

The student is required to undertake a project or write a term paper in this course. A final examination covering the main concepts of the course is also given.

Teaching Professors Names: 
Casimir Kulikowski
Vladimir Pavlovic