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One classic problem is the Coin Change Problem. Given a target value and a set of coin denominations, the goal is to find the minimum number of coins needed to make up the target value. This problem can be solved using dynamic programming by breaking it down into subproblems.

Another classic problem is the Longest Increasing Subsequence Problem. Given an array of numbers, the task is to find the longest increasing subsequence (a subsequence in which the elements are in increasing order). This problem can also be solved using dynamic programming.

A third classic problem is the 0/1 Knapsack Problem. Given a set of items with their respective weights and values, and a knapsack with a maximum weight capacity, the goal is to determine the maximum value of items that can be included in the knapsack without exceeding its weight capacity. This problem can be solved using dynamic programming as well.

In the Classic Dynamic Programming Problems Fill In lesson, you will learn how to solve these classic problems using dynamic programming techniques.

The Coin Change Problem requires finding the minimum number of coins needed to make up a target value. This problem can be solved using dynamic programming by breaking it down into subproblems. Each subproblem involves finding the minimum number of coins needed to make up a smaller target value.

In the Longest Increasing Subsequence Problem, you are given an array of numbers and need to find the longest increasing subsequence. This can be solved using dynamic programming by breaking it down into subproblems. Each subproblem involves finding the length of the longest increasing subsequence ending at a specific index in the array.

The 0/1 Knapsack Problem involves finding the maximum value of items that can be included in a knapsack without exceeding its weight capacity. This problem can be solved using dynamic programming by breaking it down into subproblems. Each subproblem involves finding the maximum value that can be obtained using a subset of the items and a specific weight capacity.

By understanding how to apply dynamic programming techniques to these classic problems, you will gain a deeper understanding of the power and versatility of dynamic programming.