- Introduction
- Linear regression
- Linear Methods for classification (regression of indicator matrix)
- Linear discriminant analysis
- Regularized discriminant analysis, reduced rank LDA
- Logistic regression
- The perceptron learning algorithm
- K-means (prototype method)
- Clustering methods (K-center, dendrogram)
- LVQ and k-nearest-neighbor
- Classification and Regression Trees (I)
- Classification and Regression Trees (II)
- Brief introduction to bagging and boosting
- Mixture Model
- Mixture discriminant analysis
- Hidden Markov models

- Random forest
- Support vector machine
- Nonlinear dimension reduction, manifold learning
- Nonparametric density estimation
- Spectral graph partitioning
- Mode-based clustering
- D2-clustering
**Markov random field, 2-D (Spatial) Hidden Markov Model**

- Aerial image segmentation
- Semantic classification of photographs
- Newsgroup data
- Data sets taken from the UCI machine learning database repository :

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