Modern Machine Learning: Applications and Methods

Abstract

Machine Learning (ML) is now omnipresent in most fields of human knowledge. Despite this, it remains mysterious and, to some extent, a black box of statistical methods many have heard of, but few know in detail. Although arguably some minimal degree of experience in statistics is needed to master ML, it is not impossible at all to understand many important concepts with no mathematical formalism. In this chapter, we aim at providing a description as complete as possible of what ML is and which is the main methodology used nowadays, from the most basic methods, such as support vector machines, to the very popular neural networks, which appear in some of the trendiest applications of ML. The second part of the chapter will revolve around how ML is used in different areas of knowledge, from industry to basic science. This will be done, once again, with no mathematical formalism, but instead referring to the methods presented in the first part of the chapter. Our goal is that, once a reader has gone through the full chapter, they have a basic idea of what happens behind the scenes when a ML algorithm is run, which are the main types of algorithms that one can use, and they are familiar with the extremely wide range of applications of ML in the modern world.