Undergraduate Courses

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:
Download this file (syl5935-Fall14.pdf)Syllabus[Fall 2014]106 kB
Download this file (ISC4933-2014-08.doc)ISC4933-2014-08.doc[Syllabus]40 kB