MACHINE LEARNING

Ridge regression adds which term to the loss function?
(i) L1 norm
(ii) Entropy
(iii) L2 norm
(iv) Cross-entropy

Maximum Likelihood estimation finds
(i) Prior probabilities
(ii) Parameters that maximize data likelihood
(iii) Decision boundaries
(iv) Cluster centroids

A company wants to predict the sales (y) of a product based on its advertising expenditure (x).
The observed data for 5 months is:

x (Advertising)	y (Sales)
1	3
2	4
3	2
4	5
5	7

Calculate the regression coefficients for the linear regression model:

$$y = w_0 + w_1 x$$

Then, predict the value of y when x = 6.

Given a dataset with two features: X = [[2, 0], [0, 1], [0, 2]]. Compute the covariance matrix, eigenvalues, and the principal components.