Computer Science

General objectives: to acquire basic knowledge in probability theory. Specific objectives: Knowledge and understanding: at the end of the course, students will be able to use basic notions in combinatorics to solve math problems, derive laws for discrete random variables. Applying knowledge and understanding: at the end of the course, students will be able to solve simple problems in discrete probability, problems concerning discrete random vectors and random numbers represented by continuous random variables. They will understand the role of indipendence and conditioning in discrete models and understand the meaning of some limit theorems, like the law of large numbers. Critical and judgmental skills: students will have the bases to analize and to build simple probabilistic models for physics, biology and technology, simulate discrete probability distribution, as well as the Gaussian distribution and understand the use of some elementry tools in statistics, like inference, sampling and simulation. Communication skills: ability to expose the contents of the course in the oral part of the test and in any theoretical questions present in the written test. Learning skills: the acquired knowledge will allow a study, individual or given in a course related to more specialized aspects of probability theory.