To develop a more intuitive understanding for machine learning models, let’s walk through a quick example. Let’s say we wanted to determine how to predict exam performance based on the number of hours of sleep we get and the number of hours we study the previous day. We collect a lot of data, and for each data point , we record the number of hours of sleep we got (), the number of hours we spent studying (), and whether we performed above or below the class average. Our goal, then, might be to learn a model with parameter vector
x = [x1 x2]T
The notation here means a 1x2 matrix transposed, which makes it a 2x1 matrix. It's likely that this is used, here, because a 2x1 matrix would look odd on a printed page.
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