​Help readers understand the mathematical interpretation of  learning algorithms

Teach the basics of linear algebra, probability, and data distributions and how they are essential in formulating a learning algorithm
Help readers construct and modify their own learning algorithms, such as ridge and lasso regression, decision trees, boosted trees, k-nearest neighbors, etc