Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.
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.
