Chapter 1: Introduction, Discrete Probability Spaces, Min-Cut

Chapter 2: Random Variables, Expectation, Discrete Probability Distributions, Variance, Standard Deviation, Chebyshev Inequality, Coupon Collector

Chapter 4ALarge Deviation Bounds Chapter 4B: Packet Routing on the n-cube

Chapter 6: The Probabilistic Method (and Hashing)

Chapter 12: Martingales

Chapters 10-11: The Monte Carlo Method

Chapter 14A: Statistical Learning - Learning from Examples

Chapter 14B: VC dimension and Uniform Convergence

Last Updated: Mar 31 2014