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BIST-531 Pattern Recognition
Spring only
The course will introduce the student to the fundamentals of pattern recognition and its application in extracting biological knowledge from high dimensional and low sample-size data. The course will discuss several supervised and unsupervised algorithms and how they can be applied for various purposes including feature extraction, feature selection, dimensionality reduction, clustering, and classification. Particular emphasis will be given to computational methods such as linear discriminant functions, nearest neighbor rule, weighed voting, artificial neural networks, fuzzy logic, support vector machines, genetic algorithms, and swarm intelligence. The course will present some examples of pattern recognition problems in genomics and proteomics (e.g., DNA base calling, analysis of microarray and mass spectral data, etc.) where pattern recognition methods offer a solution.
Credits: 3
Prerequisites: None
Sections:
BIST-531-01 Pattern Recognition
Spring only
The course will introduce the student to the fundamentals of pattern recognition and its application in extracting biological knowledge from high dimensional and low sample-size data. The course will discuss several supervised and unsupervised algorithms and how they can be applied for various purposes including feature extraction, feature selection, dimensionality reduction, clustering, and classification. Particular emphasis will be given to computational methods such as linear discriminant functions, nearest neighbor rule, weighed voting, artificial neural networks, fuzzy logic, support vector machines, genetic algorithms, and swarm intelligence. The course will present some examples of pattern recognition problems in genomics and proteomics (e.g., DNA base calling, analysis of microarray and mass spectral data, etc.) where pattern recognition methods offer a solution.
Credits: 3
Prerequisites: None
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