Machine Learning – perspective – Issues - Examples of Machine Learning Applications – Types of Machine Learning –Machine Learning process- preliminaries, testing Machine Learning algorithms, turning data into Probabilities, and Statistics for Machine Learning, Probability theory -Bayesian Decision Theory.

Introduction - Linear Models for Regression – Linear Regression Models and Least Squares – Subset Selection – Shrinkage Methods – Derived Input Directions - Linear Models for Classification- Discriminant Analysis – Logistic Regression – Separating Hyper planes.

Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations -Neural Networks – Fitting Neural Network - Bayesian Neural Net - Neural Network Representation – Problems – Perceptron – Multilayer Networks and Back Propagation Algorithms. Case Study: Handwriting Recognition.

Introduction - Association Rules – Apriori Algorithm - Clustering- K-means – EM Algorithm- Mixtures of Gaussians - Self-organizing Map - Principal Components, Curves and Surfaces – Independent Component Analysis. Case Study: Weather prediction.

Introduction - Single State Case - Elements of Reinforcement Learning – Model Based Learning - Temporal Difference Learning – Generalization - Partially Observable States. Case Study: Healthcare Prediction.