Lesson 1 - Basic definitions

Lesson 2 - Conditional probability

Lesson 3 - Example problems

Lesson 4 - Karger's mincut algorithm

Lesson 5 - Analysis of Karger's mincut algorithm

Lesson 6 - Random variables

Lesson 7 - Randomized quicksort

Problem solving video - The rich get richer

Problem solving video - Monty Hall problem

Lesson 1 - Bernoulli, Binomial, and Geometric distributions

Lesson 2 - Tail Bounds

Lesson 3 - Application of Chernoff bound

Lesson 4 - Application of Chebyshev's inequality

Lesson 1 - Intro to Big Data Algorithms

Lesson 2 - SAT Problem

Lesson 3 - Classification of States

Lesson 4 - Stationary Distribution of a Markov Chain

Lesson 5 - Celebrities Case Study

Lesson 6 - Random Walks on Undirected Graphs

Lesson 8 - Reservoir Sampling

Lesson 7 - Intro to Streaming, Morris Algorithm

Lesson 9 - Approximate Median

Lesson 2 : Balls, bins, hashing

Lesson 1 : Overview

Lesson 3 : Chain hashing, SUHA, Power of Two choices

Lesson 4 : Bloom filter

Lesson 5 : Pairwise independence

Lesson 6 : Estimating expectation of continuous function

Lesson 2 - Perfect hashing

Lesson 1 - Universal hash functions

Lesson 3 - Count-min filter for heavy hitters in data streams

Problem solving video - Doubly Stochastic Transition Matrix

Problem solving video - Random Walks on Linear Structures

Problem solving video - Cat And Mouse

Problem solving video - Lollipop Graph

Lesson 1 - Estimating frequency moments

Lesson 2 - Property testing framework

Lesson 3 - Testing Connectivity

Lesson 4 - Enforce & Test Introduction

Lesson 5 - Testing if a graph is a biclique

Lesson 6 - Testing bipartiteness

Lesson 1 - Property testing and random walk algorithms

Lesson 2 - Testing if a graph is bipartite (using random walks)

Lesson 3 - Graph streaming algorithms: Introduction

Lesson 4 - Graph streaming algorithms: Matching

Lesson 5 - Graph streaming algorithms: Graph sparsification

Lesson 1 - MapReduce

Lesson 2 - K-Machine Model (aka Pregel Model)