The BRADLEY DEPARTMENT of ELECTRICAL and COMPUTER ENGINEERING

Graduate PROGRAMS

Course Information

Description

Computational methods for the identification and classification of objects. Feature extraction, feature-space representation, distance and similarity measures, decision rules. Supervised and unsupervised learning. Statistical pattern recognition: multivariate random variables; Bayes and minimum-risk decision theory; probability or error; feature reduction and principal components analysis; parametric and nonparametric methods; clustering; hierarchical systems. Syntactic pattern recognition: review of automata and language theory; shape descriptors; syntactic recognition systems; grammatical inference and learning. Artificial neural networks as recognition systems.

Why take this course?

Pattern recognition is important in many fields related to electrical and computer engineering, including signal analysis, image analysis, and communication theory.

Prerequisites

STAT 4714

The course assumes knowledge of probability and random variables, as introduced in STAT 4714.

Major Measurable Learning Objectives

  • design and implement algorithms that can perform pattern recognition
  • develop problem-specific similarity measures;
  • compute the probability of classification error when underlying probability distributions are known.

Course Topics

Topic

Percentage of Course

Review of Statistical Methods 20%
Non-Parametric Techniques 15%
Linear and Piecewise-Linear Discriminate Design 15%
Review of Automata Theory and Formal Languages 15%
Grammatical Inference; Learning in Syntactic Recognition 20%
Recognition using Artificial Neural Networks 15%