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Last updated:

September 25, 2023

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FREE

This course includes:

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Description

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)

Course Curriculum

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

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