CSCI5523: Introduction to Data Mining

3 Credits

Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent

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All Instructors

B+ Average (3.339)Most Common: A (22%)

This total also includes data from semesters with unknown instructors.

1493 students
SNWFDCBA
  • 3.92

    /5

    Recommend
  • 3.61

    /5

    Effort
  • 4.30

    /5

    Understanding
  • 3.97

    /5

    Interesting
  • 4.14

    /5

    Activities


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