Lecture
This lecture covers boosting methods, focusing on Adaboost algorithm. It explains how to iteratively build a weighted sum of weak classifiers to create a strong classifier. The instructor demonstrates the Adaboost algorithm step by step, including initializing data weights, finding classifiers that minimize weighted error, and updating weights. The lecture also includes a toy example to illustrate the concept. Additionally, it discusses the implementation of Adaboost in Python and its application in face detection using Viola & Jones' method.