Lecture 12 – Simple Linear Regression
Presented by Andrew Bray and Suraj Rampure
Content by Andrew Bray, Suraj Rampure, and Ani Adhikari
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 Introduction the linear model. |
||
| 12.2 Using calculus to derive the optimal model parameters for the simple linear regression model, when we choose squared loss as our loss function. |
12.2 | |
| 12.3 Visualizing and interpreting loss surface of the SLR model. |
12.3 | |
| 12.4 Interpreting the slope of the simple linear model. |
12.4 | |
| 12.5 Defining key terminology in the regression context. Expanding the simple linear model to include any number of features. |
12.5 | |
| 12.6 RMSE as a metric of accuracy. Multiple R-squared as a metric of explained variation. Summary. |
12.6 |