Syllabus

=CSE652: Knowledge Discovery and Data Mining= =Syllabus=

Contents

 * Overview of Data Mining
 * Definition, Original of Data Mining, Applications of Data Mining, Data Mining vs. OLAP and SQL
 * Data Preparation
 * Feature Ranking, Feature Discretization, Normalization, Outlier Detection Techniques
 * Classification
 * Classification Tree, Naïve Bayes, Neural Networks, k-NN Classifier, Support Vector Machines
 * Clustering
 * K-Means, Self-Organizing Map
 * Model Evaluation
 * Confusion Matrix, Recall and Precision, ROC Curve
 * Association
 * A-Priori Algorithm

Marks Distribution

 * Two Midterms - 30%
 * Final - 30%
 * Projects - 30%
 * Assignments - 10%