10-601 Introduction to Probability

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This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014

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
  • Conditional probabilities
  • Bayes Rule
  • MLE’s, smoothing, and MAPs
  • The joint distribution
  • Inference
  • Density estimation and classification
  • (Ziv - Not sure about this here, why bot leave to the NB class?) Naïve Bayes density estimators and classifiers
  • Conditional independence
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