This workshop will explore new directions for algebraic statistics in the realm of Bayesian statistics and statistical learning. The considered topics will cover a broad range of problems from modern statistics and machine learning for which underlying algebraic structure provides a common theme. Topics of particular interest are singular models and variational inference, invariance and equivariance in statistics and machine learning, and new interdisciplinary connections between computational algebraic geometry and machine learning.