Machine learning is the subfield of AI in which systems learn patterns from data, rather than being explicitly programmed for each case. A system trained on thousands of invoices learns to identify the tax number, amount and supplier on new invoices; one trained on absenteeism history learns to predict flight risk.
Machine learning splits into supervised (learning from labelled examples — 'this is spam, this is not'), unsupervised (discovering structure in unlabelled data — clustering customers by behaviour), and reinforcement learning (learning by trial and error — used in robotics, games). Most industrial and corporate cases use supervised learning.
In INFOS applications, ML appears in the AI layer of pplPortal (predicting absenteeism, flight risk), Document Management (document classification), and KORA Productivity (anomaly detection in production where there is sufficient instrumentation). Each implementation respects AI Act principles — explainable models, auditable training data, human oversight, no opaque black boxes.