A dot-product similarity matrix is an alternative way to represent a multidimensional data set. In other words, one can convert an n × d data matrix D into an n × n similarity matrix S = DDT (which contains n2 pairwise dot products between points). The reason is that the similarity matrix contains almost the same information about the data as the original matrix. This equivalence is the genesis of a large class of methods in machine learning, referred to as kernel methods. This chapter builds the linear algebra framework required for understanding this important class of methods in machine learning. The real utility of such methods arises when the similarity matrix is chosen differently from the use of dot products (and the data matrix is sometimes not even available).