Machine Learning BasicsIntroduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.
Ensemble Methods: Random ForestExplores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
Adaboost: Boosting MethodsExplains Adaboost algorithm for building strong classifiers from weak ones, with a focus on boosting methods and face detection.
Understanding Deep LearningExplores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.
AdaBoost: Decision StumpsExplores AdaBoost with decision stumps, discussing error rules, stump selection, and the need for a bias term.