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

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This a lecture used in the [[Syllabus for Machine Learning 10-601]]
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This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]]
  
 
=== Slides ===
 
=== Slides ===
  
 
* [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 ===
  
* None
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* Mitchell Chap 1,2; 6.1-6.3.
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* Optional: [http://www.cs.cmu.edu/~tom/mlbook/Joint_MLE_MAP.pdf Draft of Chapter 2 of Tom's new textbook]. 
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** If you find an error in this, email Tom - a reward is offered for bug-finders.
  
 
=== What You Should Know Afterward ===
 
=== What You Should Know Afterward ===
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* The Axioms of Probability
 
* The Axioms of Probability
 
* Independence, binomials, multinomials
 
* Independence, binomials, multinomials
 +
* Expectation and variance of a distribution
 
* Conditional probabilities
 
* Conditional probabilities
 
* Bayes Rule
 
* Bayes Rule
 
* MLE’s, smoothing, and MAPs
 
* MLE’s, smoothing, and MAPs
 
* The joint distribution
 
* The joint distribution
* Inference
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* How to do inference using the joint distribution
 
* Density estimation and classification
 
* Density estimation and classification
* Naïve Bayes density estimators and classifiers
 
* Conditional independence
 

Latest revision as of 17:38, 15 January 2016

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

Slides

Readings

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