Course contents: Foundations of the theory of probability. Discrete and continuous probability distributions. Random variables. Moments. Transformation of random variables. Stochastic processes. Stationarity, Wide-Sense Stationarity, Ergodicity. Power density spectrum. Filtering of WSS processes. Gaussian function and white noise. Parameter estimation. Spectral estimation.

Assessment: Examination for both theory (70%) and laboratory practice (30%). Theory: written exams at the end of the semester. It is possible that home assignments will be given, which will contribute to the final grade with a percentage ranging between 10% and 20%. Laboratory practice: Written exams at the end of the semester or home assignments or both.