Transcription of Part IV Generative Learning algorithms
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Part IV Generative Learning algorithms
CS229 Lecture notesAndrew NgPart IVGenerative Learning algorithmsSo far, we ve mainly been talking about Learning algorithms that modelp(y|x; ), the conditional distribution ofygivenx. For instance, logisticregression modeledp(y|x; ) ash (x) =g( Tx) wheregis the sigmoid func-tion. In these notes, we ll talk about a different type of Learning a classification problem in which we want to learn to distinguishbetween elephants (y= 1) and dogs (y= 0), based on some features ofan animal. Given a training set, an algorithm like logistic regression orthe perceptron algorithm (basically) tries to find a straight line that is, adecision boundary that separates the elephants and dogs. Then, to classifya new animal as either an elephant or a dog, it checks on which side of thedecision boundary it falls, and makes its prediction s a different approach. First, looking at elephants, we can buildamodel of what elephants look like.
CS229Lecturenotes Andrew Ng Part IV Generative Learning algorithms So far, we’ve mainly been talking about learning algorithms that model p(y|x;θ), the conditional distribution of y given x.
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