This lecture covers the concept of descent methods and line search, focusing on the idea of updating a point based on a step direction to minimize a function. Examples in one dimension are provided to illustrate the process, along with the intuition that descent direction assumes small steps. The lecture also discusses the importance of maintaining small steps for Taylor's theorem to apply and how even ascent directions can decrease the function with long steps.