This main theme of the course will be algebraic connections between modern statistics and computation. Participants will implement numerical algorithms and learn to use software resources in lab sessions. Topics will include solving linear systems and differential equations, LU decomposition, pseudoinverses, and finite difference methods, transformations and distributions, simulating random walks, sampling and markov chains, numerical integration, quadrature, tensors as multilinear functions, tensor decompositions, the power method, Jennrich's algorithm, Prony method, symbolic and automatic differentiation. If time permits we will work through tutorials on advanced topics in computational statistics including markov chain monte carlo, variational inference and deep learning.