This tutorial will provide an overview of recent advances in Neurosymbolic Programming. The objective in this area is to learn neurosymbolic programs, which combine elements of both neural networks and classical symbolic programs with the aim of inheriting the benefits of both. A key advantage of the neurosymbolic programmiing approach is that here, one learns models that look more like the models that domain experts write by hand in code, but that are also more expressive than classical interpretable models in machine learning. Also, neurosymbolic programs can more easily incorporate prior knowledge and are easier to analyze and verify. From the point of view of techniques, neurosymbolic programming combines ideas from machine learning and program synthesis and represents an exciting new contact point between the two communities. This tutorial will cover a broad range of basic concepts in the area, including neurosymbolic architectures, domain-specific languages, architecture/program search algorithms, meta-learning algorithms such as library learning, and applications to science and autonomy. Our panel will discuss open challenges in the field and ways in which machine learning and programming languages researchers can come together to address them. The tutorial is an abridged version of the tutorial at the Neurosymbolic Programming summer school held in July 2022.