Difference between revisions of "10-601 Introduction to Probability"

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* [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pptx Slides in Powerpoint]
 
* [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pptx Slides in Powerpoint]
 +
* [http://www.cs.cmu.edu/~wcohen/10-601/prob-tour+bayes.pdf Slides in PDF]
  
 
=== Readings ===
 
=== Readings ===

Revision as of 12:44, 13 January 2016

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

Slides

Readings

  • Mitchell Chap 1,2; 6.1-6.3.

What You Should Know Afterward

You should know the definitions of the following, and be able to use them to solve problems:

  • Random variables and events
  • The Axioms of Probability
  • Independence, binomials, multinomials
  • Expectation and variance of a distribution
  • Conditional probabilities
  • Bayes Rule
  • MLE’s, smoothing, and MAPs
  • The joint distribution
  • How to do inference using the joint distribution
  • Density estimation and classification
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