Інформація про курс

This course introduces the fundamental concepts of probability theory, mathematical statistics, and probabilistic processes, providing both theoretical foundations and practical applications. Students will learn how to describe and analyze random phenomena, model uncertainty, and make data-driven inferences. Topics include sample spaces, events, conditional probability, and Bayes theorem; discrete and continuous probability distributions such as binomial, Poisson, and normal; sampling distributions and the Central Limit Theorem; point and interval estimation; hypothesis testing; and regression and correlation analysis. The course also covers the basics of Markov chains and their applications. Emphasis is placed on problem-solving, interpretation of results, and connections to real-world examples in science, engineering, and data analysis.