Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.
Explores the stochastic blockmodel, spectral clustering, and non-parametric understanding of blockmodels, emphasizing metrics for comparing graph models.
Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.
Explores the nearest neighbor classifier method, discussing its limitations in high-dimensional spaces and the importance of spatial correlation for effective predictions.