Q4.

You were just hired by MetaMind. MetaMind is expanding rapidly, and you decide to use

your machine learning skills to assist them in their attempts to hire the best. To do so, you

have the following available to you for each candidate i in the pool of candidates Z: (i) Their

GPA, (ii) Whether they took Data Mining course and achieved an A, (iii) Whether they took

Algorithms course and achieved an A, (iv) Whether they have a job offer from Google, (v)

Whether they have a job offer from Facebook, (vi) The number of misspelled words on their

resume. You decide to represent each candidate i € I by a corresponding 6-dimensional

feature vector f(z)). You believe that if you just knew the right weight vector w R you

could reliably predict the quality of a candidate i by computing w- f(z). To determine w

your boss lets you sample pairs of candidates from the pool. For a pair of candidates (k, 1)

you can have them face off in a "DataMining-fight." The result is score (k > 1), which tells

you that candidate k is at least score (k> 1) better than candidate 1. Note that the score

will be negative when I is a better candidate than k. Assume you collected scores for a set

of pairs of candidates P.

Describe how you could use a perceptron based algorithm to learn the weight vector w. Make

sure to describe the basic intuition; how the weight updates will be done; and pseudo-code

for the entire algorithm.