The goal of the course is to enrich students' statistical toolbox, allowing for more flexible data analysis and inference. Together, we will go over basic concepts in probability, Bayes' rule, theory of scientific inference, Monte Carlo Markov Chains, Neyman-Pearson approach, model comparison, highest density intervals, and a Bayesian adaptation of the Generalized Linear Model. Each lecture will be accompanied by a hands-on Matlab problem-set, to be solved by the students.