Data Mining - Class Notes

✉ cesar.acostamejia@gmail.com
  • Lab 1: Introduction to Data Mining
    • Lab 2: Random Variables (Univariate/Multivariate)
      • Lab 3: Statistical Inference (CI and H. tests)
        • Lab 4: Unsupervised learning - Principal Components, Dim. Reduction
          • Lab 5: Unsupervised learning - Clustering (K-Means, Hierarchical clustering)
            • Lab 6: Data Visualization
              • Lab 7: Unsupervised learning - Clustering (DBSCAN, Model-based clustering)
                • Break
                  • Lab 9: Unsupervised learning - Association Rules
                    • Break
                      • Lab 11: Classification Models 1 - KNN, Naive Bayes
                        • Lab 12: Classification Models 2 - Discriminant Analysis
                          • Lab 13: Classification Models 3 - Rule Learners
                            • Lab 14: Classifying unbalanced Data (Sensitivity, ROC)
                              • Lab 15: Spatial Data Visualization