Explain the bias-variance tradeoff in machine learning. How does it relate to overfitting and underfitting?

Derive the normal equation for linear regression. What are its advantages and disadvantages compared to gradient descent?

What is the kernel trick in SVM? Explain with an example of a non-linearly separable dataset.

Compare and contrast K-Means clustering with Hierarchical clustering. When would you prefer one over the other?