This lecture explores the geometric interpretation of Newton's method in the context of equations and optimization. It delves into how a local linear model is calculated at each iteration, which happens to be a quadratic model of the objective function. Through examples, the instructor illustrates how Newton's method quickly converges towards local minima in some cases, but faces challenges when the quadratic model is a poor approximation or unbounded due to the function's concavity.