Lecture 17 – Bias and Variance
Presented by Fernando Perez and Ani Adhikari
Content by Fernando Perez, Ani Adhikari, Suraj Rampure
Important: You may want to review Lecture 3 for a refresher on random variables.
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 | |
|---|---|---|
| 17.1 Variance of random variables. Walking through an alternate calculation of variance. Variance of a linear transformation. |
17.1 | |
| 17.2 Deriving the variance of a sum. Understanding covariance, correlation, and independence. |
17.2 | |
| 17.3 Variance of an i.i.d. sum. Variance of the Bernoulli and binomial distributions. |
17.3 | |
| 17.4 Variability of the sample mean. Reviewing inferential concepts from Data 8, but with the framework of random variables. |
17.4 | |
| 17.5 Introducing the data generating process and prediction error. Model risk. |
17.5 | |
| 17.6 Looking at different sources of error in our model – observation variance, model variance, and bias – and discussing how to mitigate them. |
17.6 | |
| 17.7 Decomposing model risk into the sum of observation variance, model variance, and the square of bias. |
17.7 |