Measure of Chance

A first course

Measure
of Chance

A first course in probability. From the counting you already know, to the ideas machine learning actually uses.

Raghu Ugare

LambdaMatters

raghu.ugare@gmail.comraghu.ugare@lambdamatters.com

Open chapter ITry a lab
Ω = {1,2,3,4,5,6}1odd2even3odd4even5odd6even

Contents

The course

Written as a foundation for machine learning: likelihood, information, and estimation sit in the last third, but they are prepared from the first die.

  1. ISample Spaces
  2. IIEvents and Axioms
  3. IIIConditional Probability
  4. IVBayes' Theorem
  5. VIndependence
  6. VIRandom Variables
  7. VIIExpectation and Variance
  8. VIIIDiscrete Families
  9. IXContinuous Chance
  10. XJoint Distributions
  11. XILimit Theorems
  12. XIIProbability in Learning
  13. XIIIEntropy and Information
  14. XIVEstimation
  15. XVFurther Reading

Work by hand

Labs & problems

Five interactive figures — a die, a clinic, a binomial, a settling average, a bell — and a problem set with full solutions, one cluster per chapter.

Problems & solutions

The inks

Pantone matching

Yves Klein’s IKB is the accent — not a Pantone chip, but the nearest coated neighbour is 2728 C, used here for hover. The page is warmed with Peach Fuzz and Cloud Dancer, sparked by Living Coral and Illuminating. Screen values only: paper inks still live in a fan deck.

  • Klein Blue

    IKB · near 2728 C

  • Peach Fuzz

    13-1023

  • Living Coral

    16-1546

  • Illuminating

    13-0647

  • Cloud Dancer

    11-4201