  Machine learning has been a fast growing field of research in several areas
dealing with large datasets. We report recent attempts to use Renormalization
Group (RG) ideas in the context of machine learning. We examine coarse graining
procedures for perceptron models designed to identify the digits of the MNIST
data. We discuss the correspondence between principal components analysis (PCA)
and RG flows across the transition for worm configurations of the 2D Ising
model. Preliminary results regarding the logarithmic divergence of the leading
PCA eigenvalue were presented at the conference and have been improved after.
More generally, we discuss the relationship between PCA and observables in
Monte Carlo simulations and the possibility of reduction of the number of
learning parameters in supervised learning based on RG inspired hierarchical
ansatzes.
