Classical probability, axioms of probability, basic properties of probability, models based on symmetric probability spaces, combinatorics for the purpose of probability, conditional probability, independence of events, Bernoulli trials, Bayes formula, discrete random variables, continuous random variables, expectation and variance, independence of random variables, most important distributions (e.g. binomial, geometric, Poisson, exponential, uniform. normal), law of large numbers, central limit theorem, normal approximation, very basics of discrete 2-dimensional distributions, covariance, correlation. Moreover, experimental analysis of distributions by using computers and R-software.