1.1. The Basic Principles of Active Machine Learning Figure illustrates the general workflow of active machine learning algorithms with first the initialization followed by an iterative loop consisting of three phases. The critical first step of initialization consists of clearly defining the research problem as either the modeling of an output (active learning) or the optimization of an objective (Bayesian optimization). An example of active learning is the investigation of the effect of reaction conditions, such as temperature and pressure, on the conversion, whereas with Bayesian optimization the goal is to find the optimal reaction conditions to maximize this conversion. In both cases a design space is set up which defines the ranges of the studied variables by considering the objectives and the intrinsic limitations of the experimental tools. A machine learning model is then initialized and trained using a small sample of labeled data, being experiments of which the outcome is known, stemming from literature, previous experiments, or newly performed experiments. Generally, the amount of preliminary labeled data is very low.