Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Delves into regression analysis, emphasizing linear predictors' role in approximating outcomes and discussing generalized linear models and causal inference techniques.
