The irrefutable success of deep learning on images and text has sparked significant interest in its applicability to 3D geometric data. Instead of covering a breadth of alternative geometric representations (e.g., implicit functions, volumetric, and point clouds), this course aims to take a deep dive into the discrete mesh representation, the most popular representation for shapes in computer graphics. In this course, we provide different ways of covering aspects of deep learning on meshes for the virtual audience. Our course videos outline the key challenges of using deep learning on irregular mesh representation and the key ideas on how to combine machine learning with classic geometry processing to build better geometric learning algorithms. This course note complements the course videos by providing a brief history from image convolution to mesh convolutions and extended discussion on important works on this subject. Lastly, our course website (https://anintroductiontodeeplearningonmeshes.github.io/) offers a toy dataset, mesh MNIST, and some hands-on exercises to cover the actual implementation details. Our goal is to provide a permanent virtual resource that contains a combination of theoretical and practical aspects, that enables easily incorporating deep learning in geometry processing research.