Machine Learning: Where to Apply in Theoretical Physics by Jim Halverson

In this talk I will provide a birds-eye-view of some results in machine learning and theoretical physics from the last year, including their motivation and techniques. Topics discussed will include machine learning for Calabi-Yau metrics, knot theory, lattice-QCD, and a correspondence between QFT and neural networks.