Lecture
This lecture presents a worked example of dynamic programming to solve a problem related to the number of ways to make change. The instructor explains the process step by step, focusing on the different coin denominations and the total amount of change. Through the slides, the audience follows the evolution of the solution, starting with the base case and gradually building up to the final result. Various scenarios are explored, illustrating the versatility and efficiency of dynamic programming in tackling combinatorial problems. The lecture concludes by highlighting the key insights gained from the example and the broader applicability of dynamic programming in algorithm design and optimization.