SDS385 STAT MODELS FOR BIG DATA

This folder contains Rcodes and solutions for the several issues dealt in the Statistical Models for Big Data seminar at UT Austin, including:

1. Big regression models
2. Regularization and sparsity in statistical models
3. Enough convex optimization to be dangerous
4. Online learning
5. Multiplicity (multiple comparisons, multiple testing) in big-data analysis
6. Matrix factorization and its applications (e.g. recommender systems, modeling covariance matrices)
7. Latent-variable models at scale
8. Big spatial models