diff --git a/hw3/hw3_10.py b/hw3/hw3_10.py
new file mode 100644
index 0000000..73b4091
--- /dev/null
+++ b/hw3/hw3_10.py
@@ -0,0 +1,51 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+
+def generate_data(N):
+    y = np.random.choice([1, -1], N)
+
+
+    x = np.empty((N, 3))
+    for index, i in enumerate(y):
+        if i == 1:
+            mean = [3, 2]
+            covariance = [[0.4, 0], [0, 0.4]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+        else:
+            mean = [5, 0]
+            covariance = [[0.6, 0], [0, 0.6]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+    return x, y
+
+def average_square_error(y, y_hat):
+    error = (y==y_hat)
+    return error.sum()/error.shape[0]
+
+if __name__ == '__main__':
+    errors = []
+    for times in range(128):
+        np.random.seed(times)
+        train_x, train_y = generate_data(256)               # (256, 3), (256, )
+        test_x, test_y = generate_data(4096)
+
+        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
+        w = pseudo_inverse_x @ train_y                      # (3)
+        
+        predict_y = train_x @ w
+        predict_y = np.sign(predict_y)
+
+        error = average_square_error(predict_y, train_y)
+        errors.append(error)
+        print(times, error)
+    
+    errors = sorted(errors)
+    median = ( errors[63] + errors[64] ) / 2 
+
+    plt.hist(errors, bins=10)
+    plt.xlabel("Ein")
+    plt.title("median: {}".format(median))
+    plt.savefig("10.png")
\ No newline at end of file
diff --git a/hw3/hw3_11.py b/hw3/hw3_11.py
new file mode 100644
index 0000000..ef13f00
--- /dev/null
+++ b/hw3/hw3_11.py
@@ -0,0 +1,89 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+
+def generate_data(N):
+    y = np.random.choice([1, -1], N)
+
+
+    x = np.empty((N, 3))
+    for index, i in enumerate(y):
+        if i == 1:
+            mean = [3, 2]
+            covariance = [[0.4, 0], [0, 0.4]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+        else:
+            mean = [5, 0]
+            covariance = [[0.6, 0], [0, 0.6]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+    return x, y
+
+def average_square_error(y, y_hat):
+    error = (y!=y_hat)
+    return error.sum()/error.shape[0]
+
+def sigmoid(s):
+    return 1/(1+np.exp(-s))
+
+def gradient(w, x, y):
+    y = y.reshape((-1, 1))
+    theta = sigmoid( - x@w * y )
+    yx = -y * x
+    result = theta * yx
+
+    grad = np.mean(result, axis=0)
+    return grad.reshape((3, 1))
+
+if __name__ == '__main__':
+    linear_regression_errors = []
+    logistic_regression_errors = []
+    
+    for times in range(128):
+
+        # generate data
+        np.random.seed(times)
+        train_x, train_y = generate_data(256)               # (256, 3), (256, )
+        test_x, test_y = generate_data(4096)
+
+        # linear regression
+        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
+        w = pseudo_inverse_x @ train_y                      # (3)
+        
+        predict_y = test_x @ w
+        predict_y = np.sign(predict_y)
+
+        error = average_square_error(predict_y, test_y)
+        linear_regression_errors.append(error)
+        print(times, error)
+
+        # logistic regression
+        lr = 0.1
+        T = 500
+        w0 = np.zeros((3, 1))
+        for iter in range(T):
+            grad =  gradient(w0, train_x, train_y)
+            w0 -= lr * grad
+        w0 = w0.reshape((3))
+
+        predict_y = test_x @ w0
+        predict_y = np.sign(predict_y)
+
+        error = average_square_error(predict_y, test_y)
+        logistic_regression_errors.append(error)
+        print(times, error)
+        print()
+    
+
+    linear_regression_errors = sorted(linear_regression_errors)
+    logistic_regression_errors = sorted(logistic_regression_errors)
+    linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64]
+    logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64]
+
+    plt.scatter(linear_regression_errors, logistic_regression_errors)
+    plt.xlabel("linear regression error")
+    plt.xlabel("logistic regression error")
+    plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median))
+    plt.savefig("11.png")
\ No newline at end of file
diff --git a/hw3/hw3_12.py b/hw3/hw3_12.py
new file mode 100644
index 0000000..65fd4f9
--- /dev/null
+++ b/hw3/hw3_12.py
@@ -0,0 +1,103 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+
+def generate_data(N):
+    y = np.random.choice([1, -1], N)
+
+    x = np.empty((N, 3))
+    for index, i in enumerate(y):
+        if i == 1:
+            mean = [3, 2]
+            covariance = [[0.4, 0], [0, 0.4]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+        else:
+            mean = [5, 0]
+            covariance = [[0.6, 0], [0, 0.6]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+    return x, y
+
+def generate_outliers(N):
+    y = np.ones((N))
+
+    x = np.empty((N, 3))
+    for index, i in enumerate(y):
+        mean = [0, 6]
+        covariance = [[0.1, 0], [0, 0.3]]
+        x1, x2 = np.random.multivariate_normal(mean, covariance)
+        x[index] = np.array([1, x1, x2])
+    return x, y
+
+def average_square_error(y, y_hat):
+    error = (y!=y_hat)
+    return error.sum()/error.shape[0]
+
+def sigmoid(s):
+    return 1/(1+np.exp(-s))
+
+def gradient(w, x, y):
+    y = y.reshape((-1, 1))
+    theta = sigmoid( - x@w * y )
+    yx = -y * x
+    result = theta * yx
+
+    grad = np.mean(result, axis=0)
+    return grad.reshape((3, 1))
+
+if __name__ == '__main__':
+    linear_regression_errors = []
+    logistic_regression_errors = []
+    
+    for times in range(128):
+
+        # generate data
+        np.random.seed(times)
+        train_x, train_y = generate_data(256)               # (256, 3), (256, )
+        test_x, test_y = generate_data(4096)
+        outlier_x, outlier_y = generate_outliers(16)
+
+        train_x = np.concatenate((train_x, outlier_x), axis=0)
+        train_y = np.concatenate((train_y, outlier_y), axis=0)
+
+        # linear regression
+        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
+        w = pseudo_inverse_x @ train_y                      # (3)
+        
+        predict_y = test_x @ w
+        predict_y = np.sign(predict_y)
+
+        error = average_square_error(predict_y, test_y)
+        linear_regression_errors.append(error)
+        print(times, error)
+
+        # logistic regression
+        lr = 0.1
+        T = 500
+        w0 = np.zeros((3, 1))
+        for iter in range(T):
+            grad =  gradient(w0, train_x, train_y)
+            w0 -= lr * grad
+        w0 = w0.reshape((3))
+
+        predict_y = test_x @ w0
+        predict_y = np.sign(predict_y)
+
+        error = average_square_error(predict_y, test_y)
+        logistic_regression_errors.append(error)
+        print(times, error)
+        print()
+    
+
+    linear_regression_errors = sorted(linear_regression_errors)
+    logistic_regression_errors = sorted(logistic_regression_errors)
+    linear_regression_median = linear_regression_errors[63] + linear_regression_errors[64]
+    logistic_regression_median = logistic_regression_errors[63] + logistic_regression_errors[64]
+
+    plt.scatter(linear_regression_errors, logistic_regression_errors)
+    plt.xlabel("linear regression error")
+    plt.xlabel("logistic regression error")
+    plt.title("linear regression: {}\nlogistic regression: {}".format(linear_regression_median, logistic_regression_median))
+    plt.savefig("12.png")
\ No newline at end of file
diff --git a/hw3/hw3_9.py b/hw3/hw3_9.py
new file mode 100644
index 0000000..3d0466b
--- /dev/null
+++ b/hw3/hw3_9.py
@@ -0,0 +1,51 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+
+def generate_data(N):
+    y = np.random.choice([1, -1], N)
+
+
+    x = np.empty((N, 3))
+    for index, i in enumerate(y):
+        if i == 1:
+            mean = [3, 2]
+            covariance = [[0.4, 0], [0, 0.4]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+        else:
+            mean = [5, 0]
+            covariance = [[0.6, 0], [0, 0.6]]
+            x1, x2 = np.random.multivariate_normal(mean, covariance)
+            x[index] = np.array([1, x1, x2])
+    return x, y
+
+def average_square_error(y, y_hat):
+    error = y-y_hat
+    error = error ** 2
+    return error.sum()/error.shape[0]
+
+if __name__ == '__main__':
+    errors = []
+    for times in range(128):
+        np.random.seed(times)
+        train_x, train_y = generate_data(256)               # (256, 3), (256, )
+        test_x, test_y = generate_data(4096)
+
+        pseudo_inverse_x = np.linalg.pinv(train_x)          # (3, 256)
+        w = pseudo_inverse_x @ train_y                      # (3)
+        
+        predict_y = train_x @ w
+
+        error = average_square_error(predict_y, train_y)
+        errors.append(error)
+        print(times, error)
+    
+    errors = sorted(errors)
+    median = ( errors[63] + errors[64] ) / 2 
+
+    plt.hist(errors, bins=10)
+    plt.xlabel("Ein")
+    plt.title("median: {}".format(median))
+    plt.savefig("9.png")
\ No newline at end of file