Introduction: Basic principles, Applications, Challenges. Supervised learning: Linear Regression with one variable and multiple variables, Gradient Descent, Classification, Logistic Regression, Overfitting, Regularization, Support Vector Machines, Artificial Neural Networks, Perceptrons, Multilayer networks, back-propagation, Decision Trees, Ensemble methods, Unsupervised learning: Clustering (K-means, K-mediods, Hierarchical), Dimensionality reduction: Principal Component Analysis, Applications of machine learning methods.