Introduction and basic concepts. Machine learning system's components. Learning types. Approaches to ML. Different machine learning problems. Fundamental concepts: cost-functions, overfitting, regularization, cross-validation, bias-variance trade-off, curse of dimensionality. Supervised learning (Bayesian decision theory; quadratic classifiers; density estimation: parametric (Maximum Likelihood and Bayesian estimation) and non-parametric (kernel density estimation, kNN); linear and logistic regression; linear discriminative functions; neural networks; support vector machines) Unsupervised learning (k-Means Clustering; Hierarchical clustering) Dimensionality reduction: PCA , LDA.