This course focuses on the underlying principles of pattern recognition and on the methods of machine intelligence used to develop and deploy pattern recognition applications in the real world. Emphasis is placed on the pattern recognition application development process, which includes problem identification, concept development, algorithm selection, system integration, and test and validation. Machine intelligence algorithms to be presented include feature extraction and selection, parametric and non-parametric pattern detection and classification, clustering, artificial neural networks, support vector machines, rule-based algorithms, fuzzy logic, genetic algorithms, and others. Case studies drawn from actual machine intelligence applications will be used to illustrate how methods such as pattern detection and classification, signal taxonomy, machine vision, anomaly detection, data mining, and data fusion are applied in realistic problem environments. Students will use the MATLAB programming language and the data from these case studies to build and test their own prototype solutions.

Course prerequisites: 

525.414 Probability and Stochastic Processes for Engineers or equivalent. A course in digital signal or image processing is recommended, such as 525.427 Digital Signal Processing, 525.419 Introduction to Digital Image and Video Processing, 525.443 Real-Time Computer Vision, or 525.746 Image Engineering.

Course instructor: 
Baumgart

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