Lecture 11 – Introduction to Modeling
by Suraj Rampure (Summer 2020)
Make sure to complete the Quick Check questions in between each video. These are ungraded, but it’s in your best interest to do them.
Motivating examples of models.
Defining the constant model. Formalizing the notion of a parameter.
Loss functions and their purpose. Squared loss and absolute loss. Minimizing average loss (i.e. empirical risk).
Minimizing mean squared error for the constant model using calculus, to show that the sample mean is the optimal model parameter in this case.
Performing the same optimization as in the last video, but by using a non-calculus algebraic manipulation.
Minimizing mean absolute error for the constant model using calculus, to show that the sample median is the optimal parameter in this case. Identifying that this solution isn't necessarily unique.
Comparing the loss surfaces of MSE and MAE for the constant model. Discussing the benefits and drawbacks of squared and absolute loss. Recapping the "modeling process".