Catalog Entries |
Spring Sem 2018
16-Apr-2024 |
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| COSC 526 - Introduction to Data Mining |
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A comprehensive introduction to the field of data mining. Topics covered include data preprocessing, predictive modeling (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting), model evaluation techniques, clustering (hierarchical, partitional, density-based), classification, association analysis, and anomaly detection. Case studies from text mining, electronic commerce, social science, and bioinformatics are covered. All programming projects are student-designed (no standard packages permitted). Recommended Background: Programming proficiency in languages such as C, C++, or Java. Knowledge of scripting languages such as Perl or Python is very beneficial. 3.000 Credit hours 3.000 Lecture hours Levels: Graduate, Undergraduate Schedule Types: Lecture Electrical Engr & Computer Sci Department |
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