# Introduction to Multi-Armed Bandits

This online textbook provides a comprehensive introduction of Multi-Armed Bandits (MAB), covering theoretical foundations, algorithm design, and advanced applications in sequential decision-making under uncertainty. Written by [Dr. Fangli Ying](https://fangli-ying.github.io/) (ECUST) for Teaching Fellow in a Summer Camp with [Prof. Osman Yağan (CMU)](https://users.ece.cmu.edu/~oyagan/), it progresses from stochastic bandit fundamentals (UCB, ETC algorithms) to Bayesian methods (Thompson Sampling), structured bandits with hidden parameters, and adversarial settings, featuring rigorous mathematical analysis, regret bounds, and real-world case studies in recommendation systems, wireless communications, and healthcare.

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