The course introduces a variety of tools from probability theory for designing and analysing randomized algorithms, and for analysing other probabilistic problems in computer science. Techniques include basic properties of discrete random variables, large deviation bounds, and balls and urns models. Applications include counting, distributed algorithms, and average case analysis. Prerequisites: Design and analysis of algorithms and a basic course in probabilities, or equivalent. Course book: M. Mitzenmacher, E. Upfal. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press 2005.