introduction
The objective of this course is to introduce essential concepts of pattern recognition and understand its applications in the domain of multi-dimensional signal analysis, with emphasis on image- and video-based signals. This course is addressed primarily to students in computer science, engineering, and basic science disciplines. The focus will be mainly on statistical techniques in an attempt to provide a unified approach in a large number of correlated random variable problems. Neural networks will also be studied for pattern recognition and discriminant analysis and the similarities and differences between the basic approaches highlighted.
PREREQUISITES
You are expected to know basics of calculus, linear algebra, and probability/statistics. No background in pattern recognition is required.
GRADING
Assignments 30%
Paper Presentation 10%
Final Project 60%
RECOMMENDED TEXTS
Pattern Classification, Duda, Stork, and Hart, John Wiley and Sons, 2000.
Neural Networks for Pattern Recognition, Bishop, Oxford, 1995.
SUPPLEMENT TEXT
Introduction to Statistical Pattern Recognition, Fukunaga, Academic Press, 1972.