Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.
Explores linear regression with and without covariates, covering models captured by independent distributions and tools like subspaces and orthogonal projections.
Delves into regression analysis, emphasizing linear predictors' role in approximating outcomes and discussing generalized linear models and causal inference techniques.