The following topics will be covered

• Linear Regression and Logistic Regression;
• Neural networks and deep learning, including convolutional and recurrent neural networks
• Decisions trees, Random Forests, Bagging and Boosting
• Support vector machines
• Bayesian linear and logistic regression
• Boltzmann Machines
• Unsupervised learning Dimensionality reduction, from PCA to cluster models

Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.