The Math Behind Machine Learning

Let's look at several techniques in machine learning and the math topics that are used in the process. In linear regression, we try to find the best fit line or hyperplane for a given set of data points. The parameters are found by minimizing the residual sum of squares. We find a critical point by setting the vector of derivatives of the residual sum of squares to the zero vector. By the second derivative test, if the Hessian of the residual sum of squares at a critical point is positive definite, then the residual sum of squares has a local minimum there.