Transcription of CHAPTER 2 Estimating Probabilities
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CHAPTER 2 Estimating ProbabilitiesMachine LearningCopyrightc 2017. Tom M. Mitchell. All rights reserved.*DRAFT OF January 26, 2018**PLEASE DO NOT DISTRIBUTE WITHOUT AUTHOR SPERMISSION*This is a rough draft CHAPTER intended for inclusion in the upcoming secondedition of the textbookMachine Learning, Mitchell, McGraw are welcome to use this for educational purposes, but do not duplicateor repost it on the internet. For online copies of this and other materialsrelated to this book, visit the web site send suggestions for improvements, or suggested exercises, machine learning methods depend on probabilistic approaches. Thereason is simple: when we are interested in learning some target functionf:X Y, we can more generally learn the probabilistic functionP(Y|X).
tions about q, in addition to actual observed data. Algorithm 2 has several attrac-tive properties: It is easy to incorporate our prior assumptions about the value of q by ad-justing the ratio of g1 to g0. For example, if we have reason to assume that q = 0:7 we can add in g1 = 7 imaginary flips with X = 1, and g0 = 3 imaginary flips with X =0.
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