Lecture 12 – Modeling
Presented by Fernando Perez and Suraj Rampure
Content by Fernando Perez, Suraj Rampure, Ani Adhikari, Deborah Nolan, Joseph Gonzalez
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
| Video | Quick Check | |
|---|---|---|
| 12.1 Motivating examples of models. |
12.1 | |
| 12.2 Defining the constant model. Formalizing the notion of a parameter. |
12.2 | |
| 12.3 Loss functions and their purpose. Squared loss and absolute loss. Minimizing average loss (i.e. empirical risk). |
12.3 | |
| 12.4 Minimizing mean squared error for the constant model using calculus, to show that the sample mean is the optimal model parameter in this case. |
12.4 | |
| 12.5 Performing the same optimization as in the last video, but by using a non-calculus algebraic manipulation. |
12.5 | |
| 12.6 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. |
12.6 | |
| 12.7 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". |
12.7 |