Lecture
This lecture covers the Perceptron algorithm, one of the earliest iterative solutions for binary classification problems. It discusses linear separability, convergence properties, and limitations of the algorithm. The lecture also introduces the Pocket Perceptron algorithm, which improves the behavior of Perceptron for non-linearly separable data by keeping track of the best weight iterate. Examples using heart disease data demonstrate the application of Perceptron and Pocket Perceptron, showcasing empirical error rates for both reduced and full-dimensional feature vectors.