A First Approach to Machine Learning with Linear Regression

Linear regression is one of the most accessible machine learning methods which has strong roots in the field of statistics. Problems of interest consider the numerical relationship between the input variables (or features) and the output variables (or target variables). In this chapter we introduce machine learning regression analysis with the goal, to use it for inferring the functional relation between features and target variables, helping us to understand important aspects of the data. As a prerequisite, it is explained how trained models are used to make predictions. Furthermore, a number of more general concepts and notations are introduced which are also of importance for later chapters.