This chapter provides an overview of the boosting approach in machine learning, with a focus on the AdaBoost algorithm. Boosting is a method to enhance the accuracy of any learning algorithm by combining multiple weak learners. The chapter discusses recent work on boosting, including analyses of AdaBoost's training and generalization errors, its connection to game theory and linear programming, and its relationship with logistic regression. It also explores extensions of AdaBoost for multiclass classification, methods for incorporating human knowledge, and experimental applications. The introduction explains the basic concept of machine learning, using an email spam filter as an example, and highlights the difficulty of building highly accurate prediction rules. Boosting is introduced as a technique that leverages multiple rough rules to create a more accurate prediction rule. The chapter emphasizes the importance of choosing appropriate distributions for training examples and combining weak rules through majority voting. It also leaves the choice of the base learning algorithm open to allow for a general boosting procedure.This chapter provides an overview of the boosting approach in machine learning, with a focus on the AdaBoost algorithm. Boosting is a method to enhance the accuracy of any learning algorithm by combining multiple weak learners. The chapter discusses recent work on boosting, including analyses of AdaBoost's training and generalization errors, its connection to game theory and linear programming, and its relationship with logistic regression. It also explores extensions of AdaBoost for multiclass classification, methods for incorporating human knowledge, and experimental applications. The introduction explains the basic concept of machine learning, using an email spam filter as an example, and highlights the difficulty of building highly accurate prediction rules. Boosting is introduced as a technique that leverages multiple rough rules to create a more accurate prediction rule. The chapter emphasizes the importance of choosing appropriate distributions for training examples and combining weak rules through majority voting. It also leaves the choice of the base learning algorithm open to allow for a general boosting procedure.