This document discusses decision trees, which classify examples based on decisions made at internal nodes based on attribute values. It defines decision trees and how they classify examples by following branches from the root node to a terminal node. It describes how decision trees are constructed using a top-down, greedy search approach that recursively separates the training data into subsets based on the best attribute at each step until reaching a leaf node.