image/svg+xml SEIS 632: Analytics (and Visualization) g analytics analytics prediction\n(supervised) prediction (supervised) analytics->prediction\n(supervised) pattern discovery\n(unsupervised) pattern discovery (unsupervised) analytics->pattern discovery\n(unsupervised) reinforcement learning\n(semi-supervised) reinforcement learning (semi-supervised) analytics->reinforcement learning\n(semi-supervised) numeric\n(regression) numeric (regression) prediction\n(supervised)->numeric\n(regression) categorical\n(classification) categorical (classification) prediction\n(supervised)->categorical\n(classification) clustering clustering pattern discovery\n(unsupervised)->clustering -Importance of analytics-Big V's-Data units-DIKW pyramid-Analytics process-Missing values, data types, tabular data model-Analytics vs. Databases-Analytics types-Analytics examples-SAS EM-Reading:Fayyad, Piatetsky-Shapiro, and Smyth 1996 -K-Means algorithm-Distance metrics, Euclidean distance-Normalizations-Outliers-Sensitivity to initial seeds-Need to determine k-Elbow method- --Tabular data format with targets/labels column-Regression vs classification-Performance metrics: misclassification and average squared error-Data partitioning: training, validation/development, test sets-Optimizing complexity -Regression formula-Matrix formulation-Solving for w's-Linear regression vs logistic regression-Logistic function-Missing values-One-hot encoding-Input selection: forward, backward, and stepwise-Polynomial regression-Normalization-Log transformation -Decision trees, logistic regression-Root, internal, and leaf nodes-Splitting-Logworth, and other splitting metrics-Optimizing complexity, Pruning-Assessing decision trees -Clustering, density estimation dependency modeling, outlier and change detection-Applications
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  1. 632
  2. 632 So far
  3. Analytics
  4. Analytics notes
  5. overview
  6. prediction
  7. prediction notes
  8. overview
  9. categorical
  10. categorical notes
  11. overview
  12. numeric
  13. numeric notes
  14. overview
  15. pattern discovery
  16. pattern discovery notes
  17. overview
  18. clustering
  19. clustering notes
  20. Overview