10-601 Introduction to Probability

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

Slides

• Slides in Powerpoint - William.
• TBD - Ziv.

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
• Naïve Bayes density estimators and classifiers
• Conditional independence