Linear regression and gradient descent for absolute beginners

In machine learning terminology, the sum of squared error is called the "cost". This equation is therefore roughly "sum of squared errors" as it computes the sum of predicted value minus actual value squared. The 1/2mis to "average" the squared error over the number of data points so that the number of data points doesn't affect the function. See this explanation for why we divide by 2. In gradient descent, the goal is to minimize the cost function. We do this by trying different values of slope and intercept.