Scope: Deterministic and probabilistic models. Basics of probability theory: random experiments, axioms of probability, conditional probability and independence. Random variables: cumulative distribution and probability density functions, functions of a random variable, expected values, Markov and Chebyshev inequalities, goodness-of-fit tests, transform methods. Multiple random variables: vector random variables, independence, joint cdf and pdf, conditional probability and expectation, functions of multiple random variables, jointly Gaussian random variables. Sums of random variables: the laws of large numbers, central limit theorem, confidence intervals.