The book "Introduction to Multi-Armed Bandits" by Aleksandrs Slivkins provides a comprehensive and accessible introduction to the field of multi-armed bandits, a powerful framework for decision-making under uncertainty. The book is structured into several chapters, each focusing on a specific line of research, including IID rewards, adversarial rewards, contextual bandits, and connections to economics. Key topics covered include basic models, lower bounds, Bayesian priors, Lipschitz rewards, full feedback, and adversarial costs. The author emphasizes fundamental ideas and elementary proofs, making the material suitable for students with a background in algorithms, machine learning, and probability/statistics. The book also includes exercises and literature reviews to enhance understanding and further exploration. The content is based on the author's graduate course at the University of Maryland and Columbia University, and it aims to bridge the gap between theoretical foundations and practical applications, such as web optimization, recommendation systems, and economic modeling.The book "Introduction to Multi-Armed Bandits" by Aleksandrs Slivkins provides a comprehensive and accessible introduction to the field of multi-armed bandits, a powerful framework for decision-making under uncertainty. The book is structured into several chapters, each focusing on a specific line of research, including IID rewards, adversarial rewards, contextual bandits, and connections to economics. Key topics covered include basic models, lower bounds, Bayesian priors, Lipschitz rewards, full feedback, and adversarial costs. The author emphasizes fundamental ideas and elementary proofs, making the material suitable for students with a background in algorithms, machine learning, and probability/statistics. The book also includes exercises and literature reviews to enhance understanding and further exploration. The content is based on the author's graduate course at the University of Maryland and Columbia University, and it aims to bridge the gap between theoretical foundations and practical applications, such as web optimization, recommendation systems, and economic modeling.