Spring 2016 textbook list
The Spring 2016 ECE textbook list is available online for students.
Current Prerequisites & Course Offering
For current prerequisites for a particular course, and to view course offerings for a particular semester, see the Virginia Tech Course Timetables.
ECE 5524 Pattern Recognition (3C)
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.
Pattern recognition is important in many fields related to electrical and computer engineering, including signal analysis, image analysis, and communication theory.
Typically offered: Spring. Program Area: Computers.
Prerequisites: STAT 4714.
The course assumes knowledge of probability and random variables, as introduced in STAT 4714.
Department Syllabus Information: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.
|Review of Statistical Methods||20%|
|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%|