10-601 Introduction to Probability

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



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