Explores AdaBoost with decision stumps, discussing error rules, stump selection, and the need for a bias term.
Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.
Explains Adaboost algorithm for building strong classifiers from weak ones, with a focus on boosting methods and face detection.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Covers weak learners in boosting, AdaBoost algorithm, drawbacks, simple weak learners, boosting variants, and Viola-Jones Haar-Like wavelets.
