Lecture
This lecture covers the mathematical foundations of atomistic machine learning, focusing on equivariant structural representations. It discusses the construction of λ-SOAP kernels, regression on spherical components, and the importance of representing target properties in the spherical basis. The instructor presents the concept of invariant and covariant structural representations, emphasizing the need for equivariance. Various papers in the field are referenced to illustrate the practical applications of these concepts.