added comments for gradient descent algorithm

Author: Quinn Liu

README.md

@@ -5,8 +5,7 @@ Definition of Machine Learning by Tom Mitchell
 
 "A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E."
 
-- folder ```imagesForExplanation```
-    + contains images used in other folder's README files for explanation
+- folder ```imagesForExplanation``` = contains images used in other folder's README files for explanation
 
 - folder ```supervisedLearning```
     + SUPERVISED Learning = teach the computer how to learn

imagesForExplanation/CostFunctionExampleWithTheta_0AndTheta_1.jpg

@@ -11,4 +11,7 @@ supervisedLearning
     - WHOLE POINT: Find theta_0 & theta_1 so that h(x) is close to y for our training examples (x,y)
     - mathamatically this means we need to minimize (1/2m)(Sum from i = 1 to m of (h(x^(i))-y^(i))^2 ) where (1/2m) makes math easilier and (h(x^(i)) = theta_0 + theta_1 * x^(i)
     - math notation = minimize over theta_0, theta_1 the cost function J(theta_0, theta_1) also called the squared error function
-    - WHOLE POINT explained using a picture: https://github.com/quinnliu/MachineLearning/blob/master/imagesForExplanation/CostFunctionExampleWithTheta_0AndTheta_1.jpg 
+    - WHOLE POINT explained using a picture: https://github.com/quinnliu/MachineLearning/blob/master/imagesForExplanation/CostFunctionExampleWithTheta_0AndTheta_1.jpg 
+    - Now plugging in the minimal theta_0 and theta_1 our function h(x) = theta_0 + theta_1 * x will predict h(x) = y by giving it an input x.
+
+    - GRADIENT DESCENT = algorithm that lets us find a minimal theta_0 and theta_1