STAT557/IST557: Data Mining
Course Material


Lecture notes
  1. Introduction
  2. Linear regression
  3. Linear Methods for classification (regression of indicator matrix)
  4. Linear discriminant analysis
  5. Regularized discriminant analysis, reduced rank LDA
  6. Logistic regression
  7. The perceptron learning algorithm
  8. K-means (prototype method)
  9. Clustering methods (K-center, dendrogram)
  10. LVQ and k-nearest-neighbor
  11. Classification and Regression Trees (I)
  12. Classification and Regression Trees (II)
  13. Brief introduction to bagging and boosting
  14. Mixture Model
  15. Mixture discriminant analysis
  16. Hidden Markov models



Survey of Special Topics
  1. Random forest
  2. Support vector machine
  3. Nonlinear dimension reduction, manifold learning
  4. Nonparametric density estimation
  5. Spectral graph partitioning
  6. Mode-based clustering
  7. D2-clustering
  8. Markov random field, 2-D (Spatial) Hidden Markov Model


Projects and Survey



Data



Jia Li
Last modified: Tue August 4 11:04:21 EDT 2014