In this section, we explain a few basic methods. Explaining the simple machine learning methods done in the first place makes it easier to understand the more complex ones. All the methods presented in this chapter are supervised methods. We start with linear classifiers, such as the Fisher classifier. To understand the linearity of the classifiers, we discuss the k nearest neighborhood method that is not linear by design and compare it to Fisher’s Linear Discriminant method. The last part of the linear section is dedicated to two regression methods: linear and logistic regression.