'Dan klein' presentation slideshows

CS 188: Artificial Intelligence Fall 2008

590 views • 43 slides

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*

691 views • 40 slides

I256 Applied Natural Language Processing Fall 2009

475 views • 34 slides

Learning and Inference for Hierarchically Split PCFGs

418 views • 33 slides

Quiz 7: MDPs

405 views • 27 slides

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?

505 views • 20 slides

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.

288 views • 18 slides