mot.la
Machine Learning Checklist
This post is a work in progress.
I am taking Stanford Univerisity's Machine Learning course with Andrew Ng's on Coursera, and beginning to learn about the various formulas available to a Machine Learning Engineer. This post will serve as my attempt to more quickly determine which formula might be a good fit.

Is it supervised learning?
 Can linear regression be applied?
 Is it unsupervised learning?
 If alpha (learning rate) is very small then gradient descent can be slow. If alpha is very large then gradient descent will be less accurate.
 Gradient descent can still converge to a local minimum, even with alpha (learning rate) fixed. This is because the derivative (the slope of the tangential line) is smaller each time.
 Gradient descent will scale better than the Normal Equations Method to solve for the global minimum of the cost function.
 A vector is a matrix that has only one column.
 Scalar multiplication is the act of multiplying a matrix by a number.
 You can only add 2 matrices of the same dimension.
 Matrix multiplication is used to apply your various hypothesis (get the prediction values) which can then be put into the cost function.
 Matrices that don't have an inverse are called 'singular' or 'degenerate'.
 Plot values first. Use a graph if just 2D or 3D (1 or 2 parameters).