This chapter introduces anomaly detection—an unsupervised learning topic that has been gaining more and more attention. We will start the introduction with some basic knowledge about anomaly detection including its definition, classification, major concepts, and popular algorithms. Skipping the so-called rule-based methods, we will first present information about statistics-based methods to bridge possible gaps between the anomaly detection in machine learning and traditional anomaly detection practice. Then, more detailed information will be provided for machine learning-based anomaly detection. Considering anomaly detection is usually handled by modifying existing machine learning algorithms, the popular anomaly detection algorithms from supervised machine learning, unsupervised machine learning, and semisupervised machine learning will be discussed one by one. In the end, issues in the practice of anomaly detection will be summarized.