This chapter examines one of the most active areas where feature vectors play a central role: machine learning. The Bayes classifier is used as an entry point into the field, showing how a simple statistical technique introduced in the early 1960s is surprisingly instructive for understanding how machine learning operates more broadly. The goal is to shed light on the core principles at work and to explain how they are tweaked, adapted, and developed further into different directions. This chapter also develops the idea that contemporary information ordering represents an epistemological practice that can be described and analyzed as ‘interested reading of reality’, a particular kind of inductive empiricism.