10-601 Introduction to Probability

From Cohen Courses
Revision as of 10:39, 16 August 2013 by Wcohen (talk | contribs)
Jump to navigationJump to search

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

Slides

Readings

  • None

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
  • Conditional probabilities
  • Bayes Rule
  • MLE’s, smoothing, and MAPs
  • The joint distribution
  • Inference
  • Density estimation and classification
  • Naïve Bayes density estimators and classifiers
  • Conditional independence