'Dan klein' diaporamas de présentation

CS 188: Artificial Intelligence Fall 2008

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Quiz 5: Expectimax/Prob review

Quiz 5: Expectimax/Prob review. Expectimax assumes the worst case scenario. False Expectimax assumes the most likely scenario. False P(X,Y ) is the conditional probability of X given Y. False P(X,Y,Z)=P(Z|X,Y)P(X|Y)P(Y). True Bayes Rule computes P(Y|X) from P(X|Y) and P(Y). True*

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I256 Applied Natural Language Processing Fall 2009

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Learning and Inference for Hierarchically Split PCFGs

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Quiz 7: MDPs

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Statistical NLP Winter 2009

Statistical NLP Winter 2009. Language models, part II: smoothing. Roger Levy thanks to Dan Klein and Jason Eisner. Recap: Language Models. Why are language models useful? Samples of generated text What are the main challenges in building n-gram language models?

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CS 479, section 1: Natural Language Processing

This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License . CS 479, section 1: Natural Language Processing. Lectures #13: Language Model Smoothing: Discounting. Thanks to Dan Klein of UC Berkeley for many of the materials used in this lecture. Announcements.

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