This continues our study of linear independence. The text goes a little into abstract spaces where the elements are not vectors, but functions. There is a little bit about solving differential equations in general, namely casting a general solution as the sum of a particular solution and the null space, analogous to what we did in class for matrix solutions. In EE 213, and in differential equations in general, you would pick one among all possible solutions using boundary conditions. In the lab, you will see how in machine learning, we pick one among all possible solutions using regularization, in an effort to buy generalizability.