This chapter discusses the mathematical details of some commonly used unsupervised learning algorithms. The goal of principal component analysis is to identify the most important components that can explain most of the variance in the data. PCA helps to discover the directions in which there is maximum variance, that is, the principal directions in which data are strongly aligned. In hierarchical clustering, the clusters are generated and organized in the form of a tree structure. In non-hierarchical clustering, all the data are grouped into a set of predefined number of clusters specified by the user. Unsupervised learning has many applications in engineering, especially in automation applications. Unsupervised learning techniques such as PCA and clustering have been covered in detail in the chapter. Mathematical details and algorithms were explained using numerical examples.