Basics of Machine Learning

Machine learning consists in designing algorithms that exploit data (sometimes called observations) in order to acquire domain knowledge and perform an automated decision-making task. Contrary to most conventional computing tasks, learning algorithms are data-dependent in the sense that they build task-specific models and improve upon them using the data fed to them. Machine learning algorithms are mainly classified into four classes: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each of these classes has its own interest and peculiarities. In this book, we focus on supervised classification to illustrate and to formalize the main concepts of robust machine learning. Most of the robustness techniques we discuss however in the book can also be applied to other machine learning classes. In this chapter, we present the fundamentals of supervised learning, through the specifics of the supervised classification task, and we review some of the standard optimization algorithms used for solving this task.