diff --git a/ML.py b/ML.py
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+++ b/ML.py
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+####################################################################################
+## PROBLEM1: Gradient Descent
+## Gradient descent is a popular optimization technique to solve many
+## machine learning problems. In this case, we will explore the gradient
+## descent algorithm to fit a line for the given set of 2-D points.
+## ref: https://tinyurl.com/yc4jbjzs
+## ref: https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
+##
+##
+## input: directory of faces in ./data/1_points.csv/
+## function for reading points is provided
+##
+##
+## your task: fill the following functions:
+## evaluate_cost
+## evaluate_gradient
+## udpate_params
+## NOTE: do NOT change values of 'init_params' and 'max_iterations' in optimizer
+##
+##
+## output: cost after convergence (rmse, lower the better)
+##
+##
+## NOTE: all required modules are imported. DO NOT import new modules.
+## NOTE: references are given intline
+## tested on Ubuntu14.04, 22Oct2017, Abhilash Srikantha
+####################################################################################
+
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+def load_data(fname):
+    points = np.loadtxt(fname, delimiter=',') 
+    y_ = points[:,1]
+    # append '1' to account for the intercept
+    x_ = np.ones([len(y_),2]) 
+    x_[:,0] = points[:,0]
+    # display plot
+    #plt.plot(x_[:,0], y_, 'ro')
+    #plt.xlabel('x-axis')
+    #plt.ylabel('y-axis')
+    #plt.show()
+    print('data loaded. x:{} y:{}'.format(x_.shape, y_.shape))
+    return x_, y_
+
+def evaluate_cost(x_,y_,params):
+    tempcost = 0
+    for i in range(len(y_)):
+        tempcost += (y_[i] - ((params[0] * x_[i,0]) + params[1])) ** 2 
+    return tempcost / float(10000)   
+
+def evaluate_gradient(x_,y_,params):
+    m_gradient = 0
+    b_gradient = 0
+    N = float(len(y_))
+    for i in range(len(y_)):
+        m_gradient += -(2/N) * (x_[i,0] * (y_[i] - ((params[0] * x_[i,0]) + params[1])))
+        b_gradient += -(2/N) * (y_[i] - ((params[0] * x_[i,0]) + params[1]))
+    return [m_gradient,b_gradient]
+
+def update_params(old_params, grad, alpha):
+    new_m = old_params[0] - (alpha * grad[0])
+    new_b = old_params[1] - (alpha * grad[1])
+    return [new_m,new_b]
+
+# initialize the optimizer
+optimizer = {'init_params':np.array([4.5,2.0]) , 
+             'max_iterations':10000, 
+             'alpha':0.69908, 
+             'eps':0.0000001,
+             'inf':1e10}
+
+# load data
+x_, y_ = load_data("./data/1_points.csv")
+
+# time stamp
+start = time.time()
+
+try:
+    # gradient descent
+    params = optimizer['init_params']
+    old_cost = 1e10
+    for iter_ in range(optimizer['max_iterations']):
+        # evaluate cost and gradient
+        cost = evaluate_cost(x_,y_,params)
+        grad = evaluate_gradient(x_,y_,params)
+        # display
+        if(iter_ % 10 == 0):
+            print('iter: {} cost: {} params: {}'.format(iter_, cost, params))
+        # check convergence
+        if(abs(old_cost - cost) < optimizer['eps']):
+            break
+        # udpate parameters
+        params = update_params(params,grad,optimizer['alpha'])
+        old_cost = cost
+except:
+    cost = optimizer['inf']
+
+# final output
+print('time elapsed: {}'.format(time.time() - start))
+print('cost at convergence: {} (lower the better)'.format(cost))
diff --git a/binary2deci.py b/binary2deci.py
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+++ b/binary2deci.py
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+#This program converts the given binary number to its decimal equivalent
+
+def binaryToDecimal(binary):
+    '''This function calculates the decimal equivalent to given binary number'''
+    binary1 = binary
+    decimal, i, n = 0, 0, 0
+    while(binary != 0):
+        dec = binary % 10
+        decimal = decimal + dec * pow(2, i)
+        binary = binary//10
+        i += 1
+    print('Decimal equivalent of {} is {}'.format(binary1, decimal))
+
+if __name__ == '__main__':
+    userInput = int(input('Enter the binary number to check its decimal equivalent: '))
+    binaryToDecimal(userInput)