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