Introduction

In the previous chapter, we were interested in getting insight into data, understanding complex phenomena through partial observations, and making informed decisions in the presence of uncertainty. Here, we are still interested in analyzing and processing data using statistical tools. However, the goal is not necessarily to understand the data, but to learn from it.

Learning from data is close to what we do as humans. From our experience, we intuitively learn general facts and relations about the world, even if we don't fully understand their complexity. The increasing computational power of computers makes them able to learn from data too. That's the heart of machine learning, a branch of artificial intelligence at the intersection of computer science, statistics, and applied mathematics.

This chapter is a hands-on introduction to some of the most basic methods in machine learning. These methods are routinely used by data scientists. We will use these methods with...