Linear Models for Classification

This lecture covers linear models for classification, starting with simple parametric models and progressing to hyperplanes in higher dimensions. It introduces logistic regression, loss functions, and empirical risk minimization. The instructor explains multi-output linear regression, gradient computation, and the logistic sigmoid function. The lecture concludes with a discussion on minimizing functions using gradient descent and the challenges of non-convex optimization.