Question 1: Linear Regression and Logistic Regression

(a) Describe the difference between Linear Regression and Logistic Regression in terms of their output.

(b) Explain the difference between Maximum a Posterior (MAP) estimates and Maximum Likelihood (ML) estimates.

(c) Explain the gradient descent algorithm.

(d) Explain what is Regularization and how it is applied in Linear Regression. Explain the problem and the Regularization solution.

(e) Explain Linear Basis Function Models for Linear Regression. Give three examples of the basis functions.