Course Contents

Pattern recognition - the act of taking raw data and making decisions based on the categories of the pattern - has applied to such diverse areas as character recognition, data mining, medical diagnosis, image processing, computer vision, bioinformatics, speech recognition, fraud detection, and stock market prediction. This course will provide underlying principles and various approaches of pattern recognition and decision making processes. The topics include diverse classifier designs, evaluation of classifiability, learning algorithms, and feature extraction and modeling. The goal of this course is to introduce students to the fundamental models of decision making in order to prepare them for applying the associated concepts to information processing.

Course Synopsis

This course develops a fundamental understanding of adaptive pattern recognition and a basic working knowledge of techniques that can be used in a broad range of applications. Inherent in adaptive patter recognition is the ability of the system to learn by supervised or unsupervised training, or by competition within a changing course environment. The effectiveness of a system depends upon its structure, adaptive properties and specifics of the application. The goal is to gain both a fundamental and working knowledge of different system and the ability to make a good selection when faced with real applications

Course Learning Outcomes

By the end of the course students are expected to: • Know the basic principles of pattern recognition theory and the main application domains • Understand the fundamental pattern recognition methods and algorithms • Apply well known algorithms to pilot problems • Select the most efficient algorithm, based on problem requirements • Design the methodology for pattern recognition problems of medium complexity