Dive into an 8-hour lecture series on Group Equivariant Deep Learning from the University of Amsterdam (2022). Explore fundamental concepts of group theory, including product, inverse, and representations. Learn about regular group convolutional neural networks and their historical development. Delve into advanced topics such as steerable kernels, basis functions, and irreducible representations. Discover the applications of group convolutions in neural networks and their importance in equivariant deep learning. Investigate SE(3) equivariant graph neural networks, tensor products, and message passing techniques. Gain insights into SO(3) irreps, Wigner-D matrices, and Clebsch-Gordan coefficients. Examine literature on 3D steerable graph neural networks, regular equivariant graph neural networks, and gauge equivariant graph neural networks.