Non-parametric regression for networks

This lecture covers Object Oriented Data Analysis (OODA) for networks, including defining mean objects, exploring object variability, fitting regression models, and choosing distances between objects. It also discusses network graphs, graph Laplacian space, extrinsic network distances, and practical projections. Applications include peptide correlations, Enron email corpus analysis, and comparing 19th-century literature by Jane Austen and Charles Dickens.