Introduction to Pattern Recognition,Tree Classifiers -Decision Trees: CART, C4.5, ID3., Random Forests. Bayesian Decision Theory. Linear Discriminants. Discriminative Classifiers: the Decision Boundary- Separability, Perceptrons, Support Vector Machines. Parametric Techniques- Maximum Likelihood Estimation, Bayesian Parameter Estimation, Sufficient Statistics. Non -Parametric Techniques-Kernel Density Estimators, Parzen Window, Nearest Neighbor Methods. Feature Selection- Data Preprocessing, ROC Curves, Class Separability Measures,Feature Subset Selection,Bayesian Information Criterion. The Curse of Dimensionality-Principal Component Analysis. Fisher Linear Discriminant, Singular Value Decomposition, Independent Component Analysis, Kernel PCA Locally Linear Embedding.Clustering-. Sequential Algorithms, Hierarchical Algorithms,Functional Optimization-Based Clustering,Graph Clustering, Learning Clustering, Clustering High Dimensional Data, Subspace Clustering,Cluster Validity Measures, Expectation Maximization, Mean Shift. Classifier Ensembles-Bagging, Boosting / AdaBoost. Graphical Models- Bayesian Networks, Sequential Models- State-Space Models, Hidden Markov Models, Context Dependent Classification. Dynamic Bayesian Networks.