Transcription of CS229 Lecture Notes
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CS229 Lecture Notes
CS229 Lecture NotesAndrew Ng(updates by Tengyu Ma)Supervised learningLet s 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)1CS229 Fall 20182 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?To 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).
1x 1 + 2x 2 Here, the i’s are the parameters (also called weights) parameterizing the space of linear functions mapping from Xto Y. When there is no risk of confusion, we will drop the subscript in h (x), and write it more simply as h(x). To simplify our notation, we also introduce the convention of letting x 0 = 1 (this is the intercept term ...
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