SML: Statistics

Content

• Probabilities

  • Bayes rule

  • Dependence independence conditional probabilities

  • Priors

  • Naive Bayes classifier

• Tail bounds

  • Chernoff / Hoeffding

  • Chebyshev

  • Gaussian

  • A/B testing

• Kernel density estimation

  • Parzen windows

  • Nearest neighbors

  • Watson-Nadaraya estimator

• Exponential families

  • Gaussian, multinomial and Poisson

  • Conjugate distributions and smoothing

  • Integrating out

Supplementary material

Slides in PDF and Keynote. If you want to extract the equations from the slides you can do so by using LaTeXit, simply by dragging the equation images into it. Moreover there are two book chapters covering the material of this class. Scribe's notes will be available once they're ready.

Videos

This is unedited video straight from a Lumix GF2 with a 14mm lens which should explain the sound and the exaggerated hands … But it should help as a supplement with the slides (YouTube typically makes the 1080i version available within 1 week of the upload).