diff --git a/codes/csharp/chapter_dynamic_programming/coin_change.cs b/codes/csharp/chapter_dynamic_programming/coin_change.cs
new file mode 100644
index 000000000..140022bad
--- /dev/null
+++ b/codes/csharp/chapter_dynamic_programming/coin_change.cs
@@ -0,0 +1,71 @@
+/**
+* File: coin_change.cs
+* Created Time: 2023-07-12
+* Author: hpstory (hpstory1024@163.com)
+*/
+
+namespace hello_algo.chapter_dynamic_programming;
+
+public class coin_change {
+    /* 零钱兑换：动态规划 */
+    public int coinChangeDP(int[] coins, int amt) {
+        int n = coins.Length;
+        int MAX = amt + 1;
+        // 初始化 dp 表
+        int[,] dp = new int[n + 1, amt + 1];
+        // 状态转移：首行首列
+        for (int a = 1; a <= amt; a++) {
+            dp[0, a] = MAX;
+        }
+        // 状态转移：其余行列
+        for (int i = 1; i <= n; i++) {
+            for (int a = 1; a <= amt; a++) {
+                if (coins[i - 1] > a) {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[i, a] = dp[i - 1, a];
+                } else {
+                    // 不选和选硬币 i 这两种方案的较小值
+                    dp[i, a] = Math.Min(dp[i - 1, a], dp[i, a - coins[i - 1]] + 1);
+                }
+            }
+        }
+        return dp[n, amt] != MAX ? dp[n, amt] : -1;
+    }
+
+    /* 零钱兑换：状态压缩后的动态规划 */
+    public int coinChangeDPComp(int[] coins, int amt) {
+        int n = coins.Length;
+        int MAX = amt + 1;
+        // 初始化 dp 表
+        int[] dp = new int[amt + 1];
+        Array.Fill(dp, MAX);
+        dp[0] = 0;
+        // 状态转移
+        for (int i = 1; i <= n; i++) {
+            for (int a = 1; a <= amt; a++) {
+                if (coins[i - 1] > a) {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[a] = dp[a];
+                } else {
+                    // 不选和选硬币 i 这两种方案的较小值
+                    dp[a] = Math.Min(dp[a], dp[a - coins[i - 1]] + 1);
+                }
+            }
+        }
+        return dp[amt] != MAX ? dp[amt] : -1;
+    }
+
+    [Test]
+    public void Test() {
+        int[] coins = { 1, 2, 5 };
+        int amt = 4;
+
+        // 动态规划
+        int res = coinChangeDP(coins, amt);
+        Console.WriteLine("凑到目标金额所需的最少硬币数量为 " + res);
+
+        // 状态压缩后的动态规划
+        res = coinChangeDPComp(coins, amt);
+        Console.WriteLine("凑到目标金额所需的最少硬币数量为 " + res);
+    }
+}
diff --git a/codes/csharp/chapter_dynamic_programming/coin_change_ii.cs b/codes/csharp/chapter_dynamic_programming/coin_change_ii.cs
new file mode 100644
index 000000000..e5acf1a3e
--- /dev/null
+++ b/codes/csharp/chapter_dynamic_programming/coin_change_ii.cs
@@ -0,0 +1,68 @@
+/**
+* File: coin_change_ii.cs
+* Created Time: 2023-07-12
+* Author: hpstory (hpstory1024@163.com)
+*/
+
+namespace hello_algo.chapter_dynamic_programming;
+
+public class coin_change_ii {
+    /* 零钱兑换 II：动态规划 */
+    public int coinChangeIIDP(int[] coins, int amt) {
+        int n = coins.Length;
+        // 初始化 dp 表
+        int[,] dp = new int[n + 1, amt + 1];
+        // 初始化首列
+        for (int i = 0; i <= n; i++) {
+            dp[i, 0] = 1;
+        }
+        // 状态转移
+        for (int i = 1; i <= n; i++) {
+            for (int a = 1; a <= amt; a++) {
+                if (coins[i - 1] > a) {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[i, a] = dp[i - 1, a];
+                } else {
+                    // 不选和选硬币 i 这两种方案之和
+                    dp[i, a] = dp[i - 1, a] + dp[i, a - coins[i - 1]];
+                }
+            }
+        }
+        return dp[n, amt];
+    }
+
+    /* 零钱兑换 II：状态压缩后的动态规划 */
+    public int coinChangeIIDPComp(int[] coins, int amt) {
+        int n = coins.Length;
+        // 初始化 dp 表
+        int[] dp = new int[amt + 1];
+        dp[0] = 1;
+        // 状态转移
+        for (int i = 1; i <= n; i++) {
+            for (int a = 1; a <= amt; a++) {
+                if (coins[i - 1] > a) {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[a] = dp[a];
+                } else {
+                    // 不选和选硬币 i 这两种方案之和
+                    dp[a] = dp[a] + dp[a - coins[i - 1]];
+                }
+            }
+        }
+        return dp[amt];
+    }
+
+    [Test]
+    public void Test() {
+        int[] coins = { 1, 2, 5 };
+        int amt = 5;
+
+        // 动态规划
+        int res = coinChangeIIDP(coins, amt);
+        Console.WriteLine("凑出目标金额的硬币组合数量为 " + res);
+
+        // 状态压缩后的动态规划
+        res = coinChangeIIDPComp(coins, amt);
+        Console.WriteLine("凑出目标金额的硬币组合数量为 " + res);
+    }
+}
diff --git a/codes/csharp/chapter_dynamic_programming/unbounded_knapsack.cs b/codes/csharp/chapter_dynamic_programming/unbounded_knapsack.cs
new file mode 100644
index 000000000..5213aa26b
--- /dev/null
+++ b/codes/csharp/chapter_dynamic_programming/unbounded_knapsack.cs
@@ -0,0 +1,64 @@
+/**
+* File: unbounded_knapsack.cs
+* Created Time: 2023-07-12
+* Author: hpstory (hpstory1024@163.com)
+*/
+
+namespace hello_algo.chapter_dynamic_programming;
+
+public class unbounded_knapsack {
+    /* 完全背包：动态规划 */
+    public int unboundedKnapsackDP(int[] wgt, int[] val, int cap) {
+        int n = wgt.Length;
+        // 初始化 dp 表
+        int[,] dp = new int[n + 1, cap + 1];
+        // 状态转移
+        for (int i = 1; i <= n; i++) {
+            for (int c = 1; c <= cap; c++) {
+                if (wgt[i - 1] > c) {
+                    // 若超过背包容量，则不选物品 i
+                    dp[i, c] = dp[i - 1, c];
+                } else {
+                    // 不选和选物品 i 这两种方案的较大值
+                    dp[i, c] = Math.Max(dp[i - 1, c], dp[i, c - wgt[i - 1]] + val[i - 1]);
+                }
+            }
+        }
+        return dp[n, cap];
+    }
+
+    /* 完全背包：状态压缩后的动态规划 */
+    public int unboundedKnapsackDPComp(int[] wgt, int[] val, int cap) {
+        int n = wgt.Length;
+        // 初始化 dp 表
+        int[] dp = new int[cap + 1];
+        // 状态转移
+        for (int i = 1; i <= n; i++) {
+            for (int c = 1; c <= cap; c++) {
+                if (wgt[i - 1] > c) {
+                    // 若超过背包容量，则不选物品 i
+                    dp[c] = dp[c];
+                } else {
+                    // 不选和选物品 i 这两种方案的较大值
+                    dp[c] = Math.Max(dp[c], dp[c - wgt[i - 1]] + val[i - 1]);
+                }
+            }
+        }
+        return dp[cap];
+    }
+
+    [Test]
+    public void Test() {
+        int[] wgt = { 1, 2, 3 };
+        int[] val = { 5, 11, 15 };
+        int cap = 4;
+
+        // 动态规划
+        int res = unboundedKnapsackDP(wgt, val, cap);
+        Console.WriteLine("不超过背包容量的最大物品价值为 " + res);
+
+        // 状态压缩后的动态规划
+        res = unboundedKnapsackDPComp(wgt, val, cap);
+        Console.WriteLine("不超过背包容量的最大物品价值为 " + res);
+    }
+}