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

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(Readings)
(Readings)
 
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* Mitchell Chap 1,2; 6.1-6.3.
 
* Mitchell Chap 1,2; 6.1-6.3.
 
* Optional: [http://www.cs.cmu.edu/~tom/mlbook/Joint_MLE_MAP.pdf Draft of Chapter 2 of Tom's new textbook].   
 
* Optional: [http://www.cs.cmu.edu/~tom/mlbook/Joint_MLE_MAP.pdf Draft of Chapter 2 of Tom's new textbook].   
** If you find an error in this, email Tom, a reward is offered for bug-finders.
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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 ===

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