Having explored how to summarize data in the previous chapter, we now turn to the principles of probability. This chapter introduces the mathematical framework for reasoning about uncertainty, a core component in many machine learning models and methods.

You will learn how to define and calculate basic probabilities, understand the concepts of sample spaces and events, and use set theory notation for probability rules. We will cover conditional probability, the idea of how the likelihood of one event changes given another has occurred (represented as $P(A|B)$), and distinguish between independent and dependent events. Finally, you'll get a conceptual introduction to Bayes' Theorem, a key result used throughout statistics and machine learning. The chapter concludes with practice problems to solidify these concepts.