Algebraic Geometry, Tensors, and Machine Learning

Statisticians usually look at a set of data over some population and try to provide models describing the data-generating process. In the last decades, algebraic geometers got involved. Algebraic geometry is the branch of mathematics studying sets of solutions to polynomial equations, called varieties, and, in some case, statistical models can be described by polynomial equations. In this project we look at Hadamard–Waring decompositions of algebraic statistical models arising in data analysis.