Lecture 11 – Introduction to Modeling
Presented by Fernando Perez and Suraj Rampure
Content by Fernando Perez, Suraj Rampure, Ani Adhikari, Deborah Nolan, Joseph Gonzalez
A random one of the following Google Forms will give you an alphanumeric code once you submit; you should take this code and enter it into the “Lecture 11” question in the “Quick Check Codes” assignment on Gradescope to get credit for submitting this Quick Check. You must submit this by Monday, October 5th at 11:59PM to get credit for it.
| Video | Quick Check | |
|---|---|---|
| 11.1 Motivating examples of models. |
11.1 | |
| 11.2 Defining the constant model. Formalizing the notion of a parameter. |
11.2 | |
| 11.3 Loss functions and their purpose. Squared loss and absolute loss. Minimizing average loss (i.e. empirical risk). |
11.3 | |
| 11.4 Minimizing mean squared error for the constant model using calculus, to show that the sample mean is the optimal model parameter in this case. |
11.4 | |
| 11.5 Performing the same optimization as in the last video, but by using a non-calculus algebraic manipulation. |
11.5 | |
| 11.6 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. |
11.6 | |
| 11.7 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". |
11.7 |