Machine learning

Machine learning (ML) entails a set of tools and structures to acquire information from data. This chapter explains a wide range of tools to learn from data originating from distinct sources. The chapter reviews established learning concepts and details some classical tools to perform unsupervised and supervised learning. Then, deep learning algorithms and their structural variations are discussed, along with their suitability to solve specific problems. Complementing the remaining chapters of the book, we highlight some recent topics about ML, such as adversarial training and federated learning, including many illustrative examples. The aim is to equip the reader with a broad view of the current ML techniques and set the stage to access the details discussed in the remaining parts of the book. This chapter presents some fundamental concepts of ML that are broadly utilized and discusses some current ongoing investigations.