Lecture 11 – Introduction to Modeling
Presented by Andrew Bray
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 | |
|---|---|---|
| 11.0 Announcements. |
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| 11.1 Defining a model. |
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| 11.2 Choosing the constant model. Formalizing the notion of a parameter. |
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| 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 |