CS229 Lecture notes - Stanford Engineering Everywhere

CS229 Lecture notesAndrew NgSupervised learningLets start by talking about a few examples of supervised learning we have a dataset giving the living areas and prices of 47 housesfrom Portland, Oregon:Living area (feet2)Price (1000$s) can plot this data:50010001500200025003000350040004500 500001002003004005006007008009001000hous ing pricessquare feetprice (in $1000)Given data like this, how can we learn to predict the prices of other housesin Portland, as a function of the size of their living areas?1CS229 Winter 20032To establish notation for future use, we ll usex(i)to denote the input variables (living area in this example), also called inputfeatures, andy(i)to denote the output ortargetvariable that we are trying to predict(price).

1 2 Xm i=1 (hθ(x(i))−y(i))2. If you’ve seen linear regression before, you may recognize this as the familiar least-squares cost function that gives rise to the ordinary least squares regression model. Whether or not you have seen it previously, lets keep going, and we’ll eventually show this to be a special case of a much broader family ...

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