Basic data mining concepts – data representation and visualization. Classification techniques: decision trees, rule-based classifier, nearest-neighbor classifier, Bayesian classifier, artificial neural networks, support vector machines. Cluster analysis: density-based cluster, graph-based cluster. Basic learning mechanisms: supervised and unsupervised. Temporal and spatial mining: prediction, time-series, regression. Performance evaluation: ROC curves, confusion matrix. Applications of data mining: anomaly detection, remote sensing, bioinformatics and medical imaging. Programming exercises will be assigned.

Attachments:
FileDescriptionFile size
Download this file (syl5935-Fall14.pdf)SyllabusFall 2014106 kB
Download this file (ISC5935-2014-08.doc)ISC5935-2014-08.docSyllabus40 kB
Download this file (ISC5935 Spring 2012.pdf)ISC5935 Spring 2012.pdfSyllabus81 kB
Dept. of Scientific Computing
Florida State University
400 Dirac Science Library
Tallahassee, FL 32306-4120
Phone: (850) 644-1010
admin@sc.fsu.edu
© Scientific Computing, Florida State University
Scientific Computing