Regression \& regularization a. You are hired by the HR department of a large company to try and predict what pay to offer to new employees based on their overall number of years of experience and their previous salary. Which machine-learning model would you use to achieve this? Explain why you would use that model. b. What is the objective function for ordinary least-squares (OLS) regression? What quantity is being minimized in OLS? What is the OLS solution? c. For Ridge regression, what is the objective function and what are the effects of this regularization method on its parameter estimates? d. For LASSO, what is the objective function and what are the effects of this regularization method on its parameter estimates? e. Bonus: What phenomenon that we discussed in class might create a problem when applying the model you suggested above in (a) to predict the current salary from the number of years of experience and the previous salary? Explain how the problem might come about and suggest a solution