# InfoCoBuild

## An Introduction to Probability in Computing

An Introduction to Probability in Computing. Instructor: Prof. John Augustine, Department of Computer Science and Engineering, IIT Madras. With the advent of machine learning, data mining, and many other modern applications of computer science, we are increasingly seeing the influence of probability theory on computer science. This course is aimed at providing a brief introduction to probability theory to CS students so that they can grasp recent CS trends more easily (from nptel.ac.in)

 Lecture 22 - Routing in Sparse Networks

Go to the Course Home or watch other lectures:

 Introduction to Probability Lecture 01 - A Box of Chocolates Lecture 02 - Axiomatic Approach to Probability Theory Lecture 03 - Verifying Matrix Multiplication: Statement, Algorithm and Independence Lecture 04 - Verifying Matrix Multiplication: Correctness, Law of Total Probability Lecture 05 - How Strong is your Network? Lecture 06 - How to Understand the World? Play with it! Lecture 07 - Tutorial 1 Lecture 08 - Tutorial 2 Discrete Random Variables Lecture 09 - Basic Definitions Lecture 10 - Linearity of Expectation and Jensen's Inequality Lecture 11 - Conditional Expectation I Lecture 12 - Conditional Expectation II Lecture 13 - Geometric Random Variables and Collecting Coupons Lecture 14 - Discrete Random Variables - Randomized Selection Tail Bounds Lecture 15 - Markov's Inequality Lecture 16 - The Second Moment, Variance and Chebyshev's Inequality Lecture 17 - Median vs Sampling Lecture 18 - Median vs Sampling - Analysis Lecture 19 - Moment Generating Functions and Chernoff Bounds Lecture 20 - Parameter Estimation Lecture 21 - Control Group Selection Applications of Tail Bounds Lecture 22 - Routing in Sparse Networks Lecture 23 - Analysis of Valiant's Routing Lecture 24 - Random Graphs