Neuroevolution, or optimization of neural networks through evolutionary computation, has gained significant momentum recently. Its primary focus is on evolving neural networks for intelligent agents when the training targets are not known, and good performance requires many decisions over time, such as robotic control, game playing, and decision-making. More recently it has also been extended to optimizing deep-learning architectures, understanding how biological intelligence evolved, optimizing neural networks for hardware implementation, and leveraging and empowering large language models. This tutorial introduces students to the basics of neuroevolution, progresses to several advanced topics that make neuroevolution more effective and more general, reviews example application areas, and proposes further research questions.