Basic Statistics

Content

• Probabilities

  • Dependence, independence, conditional probabilities

  • Bayes rule and Chain rule

  • Paradoxes in measure theory

• Parameter estimation

  • Maximum Likelihood Estimation (MLE)

  • Maximum a Posteriori Estimation (MAP)

• Application I - Naive Bayes for spam filtering

  • Discrete features in Naive Bayes

  • Estimating parameters

  • Finite sample size problems

• Application II - Naive Bayes for fMRI data processing

  • Continuous features in Naive Bayes

Supplementary material

• Slides in PDF.

• Alex Smola and S.V.N. Vishwanathan: Introduction to Machine Learning, Chapter I and II in PDF

• Patrick Billingsley: Probability and Measure (Wiley Series in Probability and Statistics)

• Larry Wasserman: All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)

• Tom Mitchell's 10701 lectures (Lectures 2,3,4)

• Aarti Singh's 10701 lectures (Lectures 2,3,4)

• Eric P. Xing's 10701 lectures (Lectures 2,3)

• Tom Mitchell: Machine Learning, Chapter I in PDF

• Andrew Moore's Basic Probability Tutorial slides in PDF

Videos

This is unedited video straight from a Lumix GF2 with a 14-42mm kit lens which should explain the sound (it doesn't have a dedicated audio input) … But it should help as a supplement with the slides.