This is a tutorial page created by members of our research group. It covers various areas of Machine Learning, Artificial Intelligence, Computational Intelligence. It is updated regularly to cover as many of the areas of research done in  CPAMI as possible.

• Fitting A Mixture Distribution to Data: Tutorial

• Eigenvalue and Generalized Eigenvalue Problems: Tutorial

• Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

• The Theory Behind Overfitting, Cross-Validation, Regularization, Bagging and Boosting: Tutorial

• Linear and Quadratic Discriminant Analysis: Tutorial

• Unsupervised and Supervised Principal Component Analysis: Tutorial

• Fisher and Kernel Fisher Discriminant Analysis: Tutorial

• Hidden Markov Model: Tutorial

• Multi-dimensional Scaling, Sammon Mapping and Isomap: Tutorial

• Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial

• Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial