Advanced Machine Learning: Bagging

This lecture covers ensemble learning methods such as Bagging, Boosting, and RANSAC, focusing on the concept of aggregation to improve model performance by combining multiple models. The instructor explains the process of bootstrapping to create diverse observation sets and the importance of selecting unstable models for Bagging. The lecture also delves into the transition from Bagging to Boosting, highlighting how Boosting builds models sequentially to correct errors made by previous models.