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

Lecture 20 - Parameter Estimation

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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