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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 12 - Conditional Expectation II

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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