Linear Models for Classification

This lecture introduces linear models for classification, focusing on logistic regression. It covers the transition from regression to classification, hyperplanes, linear regression, multi-output prediction, and the logistic sigmoid function. The instructor explains the training process for logistic regression, including the cross-entropy loss function and gradient computation. The lecture also discusses model evaluation metrics such as accuracy, precision, recall, and ROC curves. Practical examples and exercises illustrate the concepts, emphasizing the importance of decision boundaries in different classifiers.