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High-throughput experimental techniques have generated huge amount of biological data such as genome sequences, microarray expression datasets, protein-protein interaction networks, and disease-gene mapping. Data mining techniques such as classification, clustering, and model-based prediction are routinely applied to these data for pattern recognition and knowledge discovery. It is also widely applied in science, engineering, business intelligence organizations, and financial analysis. This course will focus on techniques that have been successfully applied to bioinformatics and other engineering problems and will emphasize the capability of formulating and solving problems using either existing tools or self-made programs. Real-world (bioinformatics) problems from the literature will be used to challenge students; skills of data mining learned in the course.
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