Difference between revisions of "10-601 Naive Bayes"

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=== Slides and Other Materials ===
 
=== Slides and Other Materials ===
 
* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/nb.pptx Slides in Powerpoint]
 
* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/nb.pptx Slides in Powerpoint]
* I did some examples in Matlab:
 
** [[jointDistCommands.m|A joint distribution for a die-rolling problem.]]
 
** [[roll.m|The roll subroutine used here - an example of vectorized code.]]
 
** [[gaussianJointDistCommands.m|Examples of another joint distribution.]]
 
  
 
=== Readings ===
 
=== Readings ===

Revision as of 19:03, 19 January 2016

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides and Other Materials

Readings

What You Should Know Afterward

  • What conditional independence means
  • How to implement the multinomial Naive Bayes algorithm
  • How to interpret the predictions made by the NB algorithm