At the end of the course, the student will learn:

• the fundamentals of Statistical Learning, regression and classification
• linear and nonlinear regressions including splines
• Generalised Additive Models for both regression and classification problems
• regularisation techniques including Ridge regression and the Lasso
• the tree-based methods for regression and classification
• Support Vector Machine which is highly appreciated among Data Science and Machine Learning Community
• the difference between supervised and unsupervised learning methods