# 2. Machine learning models

In this module, we learn about different machine learning models.

# Module overview

- Understand how some selected machine learning models work: linear regression, decision trees, random forests, and gradient boosting.
- Look at how they are constructed and trained.
- Learn about gradient descent, an optimization algorithm widely used to train machine learning models.
- Become familiar with learning curves, bias / variance tradeoff, regularization, decision boundaries, and ensemble of models.
- Learn different ways to optimize hyperparameters.
- Get started with ML assignment 2

## Week 38

- Work with chapter 3: Classification.
- Work with the notebook
`DAT158-Part1-3-classification.ipynb`

. - Work with assignment 1.
- Superficial reading of chapter 4.
- Look at the notebook
`DAT158-Part2-1-LinReg_GradientDescent.ipynb`

,`DAT158-Part2-2-PolyReg_learning_curves_bias-variance.ipynb`

and`DAT158-Part2-3-Regularization.ipynb`

.

## Week 40

- Presentation of assignment 1
- Go through chapter 6 and 7.
- Work with the notebooks:
`DAT158-Part2-4-Decision_Trees.ipynb`

and`DAT158-Part2-5-Ensembling-RandomForests-and-Boosting.ipynb`

- Extra material:
`DAT158-Part2-6-Extra-RandomForests-examples.ipynb`

and`DAT158-Part2-6-Extra-Hyperparameter_optimization.ipynb`

- Get started with assignment 2.