Lecture 25 – Inference for Modeling
Presented by Fernando Perez and Suraj Rampure
Content by Suraj Rampure, Fernando Perez, John DeNero, Sam Lau, Ani Adhikari, Deb Nolan
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 | |
|---|---|---|
| 25.1 A big picture overview of inference. Parameters and estimators. Bias and variance of estimators. The sample mean estimator. |
25.1 | |
| 25.2 Using bootstrap resampling in order to estimate the sampling distribution of an estimator. |
25.2 | |
| 25.3 Defining confidence intervals more generally. Describing and demoing how we can use the bootstrap to create confidence intervals for population parameters. |
25.3 | |
| 25.4 The assumptions we make when modeling with linear regression.. |
25.4 | |
| 25.5 Using the bootstrap to estimate the sampling distributions of parameters in a linear regression model. Inference for the true slope of a feature. |
25.5 | |
| 25.6 Multicollinearity, and its impacts on the interpretability of the parameters of our model. A summary of the lecture, and a brief overview of the ML taxonomy. |
25.6 |