Multiscale Methods for Machine Learning

This course focuses on teaching multiscale methods for machine learning, specifically addressing the challenge of developing models that perform well for large output spaces with limited training data. The course aims to provide learners with the skills to build a multi-scale machine learning framework called Prediction for Enormous and Correlated Output Spaces (PECOS). The teaching method involves presenting a hierarchy over the output space using unsupervised learning and learning a machine learning model that makes predictions at each level of the hierarchy. The intended audience for this course includes individuals interested in machine learning, particularly those working on problems with large output spaces and limited training data.