# Regression

> ML Prep practice set.

- Stable ID: `regression`
- Area: machine-learning
- Timebox: 45 minutes

Connect squared loss, gradients, normal equations, and ridge regularization.

## Instructions

1. Track matrix dimensions through every expression.

## Ordered items

1. [Linear regression knowledge check](https://mlprep.iwase.dev/item/google-mlcc-linear-regression-quiz/?set=regression) — `google-mlcc-linear-regression-quiz` (easy, 10 min)
2. [Linear regression objective and gradient](https://mlprep.iwase.dev/item/original-ml-regression/?set=regression) — `original-ml-regression` (easy, 8 min)
3. [Centering and ridge regression](https://mlprep.iwase.dev/item/pml-book1-ex11.2/?set=regression) — `pml-book1-ex11.2` (medium, 15 min)

## Completion

Derive the unregularized and ridge objectives and updates.

## Next recommended practice

- [Classification](https://mlprep.iwase.dev/set/classification.md)