The goal of this talk is to show how probabilistic methods can be used to accelerate standard matrix factorizations (e.g. SVD) with provable characteristics in terms of speed and accuracy.
I will focus on clustering data points in low dimensions (mostly 2d) and provide an overview of some popular clustering algorithms.
The accompanying live notebook is linked from my homepage: https://users.flatironinstitute.org/~magland
Tensor network methods are a family of variational algorithms used to simulate many body quantum systems in a variety of situations. With some brief motivation from physics, I'll explain why anyone would want to use these methods, why it is that they are so effective for certain classes of problems, and some extensions to other fields like machine learning.
