Bayesian networks are powerful graphical models that represent probabilistic relationships between variables. This talk will introduce the core concepts of Bayesian networks, explaining how they encode dependencies and enable efficient reasoning under uncertainty. We'll explore practical examples of how these networks can be used for tasks such as problem analysis, prediction, diagnosis, and decision making, demonstrating their utility in a variety of domains.