Lecture
This lecture covers the fundamentals of supervised learning, focusing on k-nearest neighbors (k-NN) and decision trees. It explains the process of learning a function from input/output pairs and the types of supervised learning tasks. The lecture delves into the techniques of k-NN, tree-based models, and the bias-variance tradeoff. It also explores examples of supervised learning applications, such as image classification and clustering. Additionally, it discusses the concepts of entropy, distance metrics, and model evaluation. The lecture concludes with an overview of ensemble methods like random forests and boosted decision trees.