Problem: What is regularization ?

Topics to be covered:

1. The concept and purpose of regularization in machine learning.
2. Different types of regularization methods (L1, L2, Elastic Net) and the scenarios in which they are applied.
3. The mathematical interpretation of regularization terms.
4. The impact of regularization on bias-variance tradeoff.
5. The role of regularization parameters and how to optimize them.
6. Regularization in relation to feature selection.
7. The effect of regularization on model complexity.
8. The concept of early stopping as a form of regularization in neural networks.
9. Overfitting and underfitting: How regularization helps mitigate these issues.