Linear algebra and probability theory basics – Machine learning- Types- Classification- Regression- Multi-class classification. dimensionality reduction –Linear and Logistic Regression. Naive Bayes, Parameter Estimation, Sequential Pattern Classification. Neural Network Basics – Backpropagation –Support Vector Machines, Kernel methods – Bias-Variance tradeoff. Regularization and model/feature selection. Ensemble Methods: Boosting, Bagging, Random Forests. Unsupervised learning – K-Means clustering- EM Algorithm – Reinforcement learning – introduction to deep learning. Recent Applications and trends of Machine Learning.