Applied Computer Science and Artificial Intelligence

General goals: to acquire knowledge and ability to apply basic topics of probability and statistics. Specific goals: Axioms and elementary properties of probabilities. Random variables. Continuous and discrete distributions. Expected values. Introduction to estimation theory and hypothesis testing. Knowledge and understanding: At the end of the course the student will have acquired the basic notions and results related to probability theory on finite and countable spaces, to the concept of random discrete vectors and to the concept of continuous random variable. Applying knowledge and understanding: At the end of the course the student will be able to solve simple problems in discrete probability, problems concerning discrete random vectors and random numbers represented by continuous random variables. The student will also be able to understand the meaning and implications of independence and conditioning (in the context of discrete models), to understand the meaning of some fundamental limit theorems, such as the law of large numbers. Critiquing and judgmental skills: Students will have the bases to analyse and to build simple probabilistic models for physics, biology and technology, simulate discrete probability distributions, as well as the Gaussian distribution and understand the use of some elementary 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 ability: The acquired knowledge will allow a study, individual or given in a course related to more specialized aspects of probability theory.