Introduction to Probability

This chapter introduces the foundational concepts of probability, which underpin both statistics and machine learning. Probability enables us to quantify and reason about uncertainty, allowing for informed predictions and data-driven decisions.

By the end of this chapter, you'll gain a basic understanding of important probability concepts essential for further studies in machine learning. You'll learn about different interpretations of probability, including classical, frequentist, and subjective perspectives. We'll look into fundamental terms like events, outcomes, and sample spaces, and introduce probability rules that govern how probabilities are assigned and manipulated.

Additionally, we'll cover fundamental principles such as the addition and multiplication rules, important for calculating the probability of combined events. You'll also gain insight into conditional probability and the concept of independence, both needed for understanding more complex probabilistic models.

Throughout this chapter, we'll use straightforward examples and simple exercises to solidify your understanding. By the end, you'll have a solid grasp of the essential probability concepts that are big deals for getting into statistics and machine learning.