NOC:An Introduction to Probability in Computing


Lecture 1 - Introduction to Probability - A box of chocolates


Lecture 2 - Introduction to Probability - Axiomatic Approach to Probability Theory


Lecture 3 - Introduction to Probability - Verifying Matrix Multipilication (Statement,Algorithm and Independence)


Lecture 4 - Introduction to Probability - Verifying Matrix Multipilication (Correctness and Law of Total Probability)


Lecture 5 - Introduction to Probability - How Strong is your Network?


Lecture 6 - Introduction to Probability - How to Understand the World? Play with it!


Lecture 7 - Tutorial 1


Lecture 8 - Tutorial 2


Lecture 9 - Discrete Random Variables - Basic Definitions


Lecture 10 - Discrete Random Variables - Linearity of Expectation and Jensens Inequality


Lecture 11 - Discrete Random Variables - Conditional Expectation I


Lecture 12 - Discrete Random Variables - Conditional Expectation II


Lecture 13 - Discrete Random Variables - Geometric Random Variables and Collecting Coupons


Lecture 14 - Discrete Random Variables - Randomized Selection


Lecture 15 - Tail Bounds I - Markov's Inequality


Lecture 16 - Tail Bounds I - The Second Moment,Variance and Chebyshev's Inequality


Lecture 17 - Tail Bounds I - Median via Sampling


Lecture 18 - Tail Bounds I - Median via Sampling - Analysis


Lecture 19 - Tail Bounds I - Moment Generating Functions and Chernoff Bounds


Lecture 20 - Tail Bounds I - Parameter Estimation


Lecture 21 - Tail Bounds I - Control Group Selection


Lecture 22 - Applications of Tail Bounds - Routing in Sparse Networks


Lecture 23 - Applications of Tail Bounds - Analysis of Valiant's Rounting


Lecture 24 - Applications of Tail Bounds - Random Graphs


Lecture 25 - Live Session 2


Lecture 26 - Live Session