This appendix provides a concise introduction to key machine learning techniques employed throughout the book. It focuses on two main areas: unsupervised learning and Bayesian classification. The appendix begins with an exploration of K-means clustering, a fundamental unsupervised learning algorithm, demonstrating its application to network community detection. It then discusses methods for evaluating unsupervised learning techniques, including confusion matrices and the adjusted Rand index. The silhouette score is introduced as a metric for assessing clustering quality across different numbers of clusters. The appendix concludes with an explanation of the Bayes plugin classifier, a simple yet effective tool for network classification tasks.