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.
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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