Machine learning often involves finding the best parameters for a model. This process typically relies on optimization techniques grounded in calculus. This chapter establishes why understanding calculus is beneficial for working with machine learning algorithms.

We begin by looking at how machine learning models can be viewed as mathematical functions. Then, we examine the core concept of optimization: finding the parameter values that make a model perform best according to some metric. Central to this is the idea of measuring how the model's performance changes as its parameters are adjusted. This is where derivatives come into play. We'll introduce the derivative as a measure of instantaneous change. Finally, we'll connect these ideas to show how calculus provides the mathematical basis for key machine learning algorithms.

This chapter covers:

• Representing machine learning models as mathematical functions.
• The fundamental concept of optimization in machine learning.
• How derivatives measure rates of change and guide optimization.
• The role of calculus as a foundational tool for understanding ML algorithms.