1. Probability theory: sample space, sigma algebra, probability measure, conditional probability, stochasticindependence, Bayes’ theorem, discrete and real random variables, probability distribution and density, joint distribution and density, functions of random variables, expectation and variance, conditional expectation, generating and characteristic function, central limit theorem, law of large numbers and Chebyshev inequality. 2. Standard stochastic models: (Bernoulli) uniform, binomial, Poisson, geometric,normal and exponential distribution. 3. Stochastic random sequences: ensemble of random variables vs. Path model, distributions and densities of random sequences, discrete random walk process, convergence of random sequences, Markov property, Markov chains. 4. Random processes: auto- and cross-correlation function, Wiener-Levy process, Poisson process, Markov processes, classification of random processes, power spectral density, Wiener-Khintchinetheorem, linear systems and random process, white Gaussian noise, deviation and integration of stochastic paths, the MSE-calculus and the Karhunen-Loeve development of random processes.