Difference between revisions of "Class meeting for 10-605 Streaming Naive Bayes"

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#REDIRECT [[Class meeting for 10-605 in Fall 2016 Streaming Naive Bayes]]
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This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2017]].
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=== Slides ===
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* [http://www.cs.cmu.edu/~wcohen/10-605/stream-and-sort.pptx Slides in Powerpoint] - the stream-and-sort pattern, and large-vocabulary Naive Bayes
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* [https://qna-app.appspot.com/edit_new.html#/pages/view/aglzfnFuYS1hcHByGQsSDFF1ZXN0aW9uTGlzdBiAgIDQy6a0CAw Today's quiz]
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=== Readings for the Class ===
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* Required: [http://www.cs.cmu.edu/~wcohen/10-605/notes/scalable-nb-notes.pdf my notes on streaming and Naive Bayes]
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* Optional:  If you're interested in reading more about smoothing for naive Bayes, I recommend this paper:  Peng, Fuchun, Dale Schuurmans, and Shaojun Wang. "Augmenting naive Bayes classifiers with statistical language models." Information Retrieval 7.3 (2004): 317-345.
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=== Things to Remember ===
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* Zipf's law and the prevalence of rare features/words
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* Communication complexity
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* Stream and sort
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** Complexity of merge sort
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** How pipes implement parallel processing
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** How buffering output before a sort can improve performance
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** How stream-and-sort can implement event-counting for naive Bayes

Revision as of 11:28, 11 August 2017

This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2017.

Slides

Readings for the Class

  • Required: my notes on streaming and Naive Bayes
  • Optional: If you're interested in reading more about smoothing for naive Bayes, I recommend this paper: Peng, Fuchun, Dale Schuurmans, and Shaojun Wang. "Augmenting naive Bayes classifiers with statistical language models." Information Retrieval 7.3 (2004): 317-345.

Things to Remember

  • Zipf's law and the prevalence of rare features/words
  • Communication complexity
  • Stream and sort
    • Complexity of merge sort
    • How pipes implement parallel processing
    • How buffering output before a sort can improve performance
    • How stream-and-sort can implement event-counting for naive Bayes
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