Applied Statistics and Machine Learning

Catalog description

Introduction to modern applied statistics emphasizing computational methods to deal with high-dimensional data. Multivariate linear and nonlinear regression, model selection, overfitting, cross-validation, bootstrapping and quantification of uncertainty in model parameters and predictions, principal component analysis, and classification.