Lecture 1 - Basic definitions
Lecture 2 - Conditional probability
Lecture 3 - Example problems
Lecture 4 - Karger's mincut algorithm
Lecture 5 - Analysis of Karger's mincut algorithm
Lecture 6 - Random variables
Lecture 7 - Randomized quicksort
Lecture 8 - Problem solving video - The rich get richer
Lecture 9 - Problem solving video - Monty Hall problem
Lecture 10 - Bernoulli, Binomial and Geometric distributions
Lecture 11 - Tail Bounds
Lecture 12 - Application of Chernoff bound
Lecture 13 - Application of Chebyshev's inequality
Lecture 14 - Intro to Big Data Algorithms
Lecture 15 - SAT Problem
Lecture 16 - Classification of States
Lecture 17 - Stationary Distribution of a Markov Chain
Lecture 18 - Celebrities Case Study
Lecture 19 - Random Walks on Undirected Graphs
Lecture 20 - Intro to Streaming, Morris Algorithm
Lecture 21 - Reservoir Sampling
Lecture 22 - Approximate Median
Lecture 23 - Overview
Lecture 24 - Balls, bins, hashing
Lecture 25 - Chain hashing, SUHA, Power of Two choices
Lecture 26 - Bloom filter
Lecture 27 - Pairwise independence
Lecture 28 - Estimating expectation of continuous function
Lecture 29 - Universal hash functions
Lecture 30 - Perfect hashing
Lecture 31 - Count-min filter for heavy hitters in data streams
Lecture 32 - Problem solving video - Doubly Stochastic Transition Matrix
Lecture 33 - Problem solving video - Random Walks on Linear Structures
Lecture 34 - Problem solving video - Lollipop Graph
Lecture 35 - Problem solving video - Cat And Mouse
Lecture 36 - Estimating frequency moments
Lecture 37 - Property testing framework
Lecture 38 - Testing Connectivity
Lecture 39 - Enforce and Test Introduction
Lecture 40 - Testing if a graph is a biclique
Lecture 41 - Testing bipartiteness
Lecture 42 - Property testing and random walk algorithms
Lecture 43 - Testing if a graph is bipartite (using random walks)
Lecture 44 - Graph streaming algorithms: Introduction
Lecture 45 - Graph streaming algorithms: Matching
Lecture 46 - Graph streaming algorithms: Graph sparsification
Lecture 47 - MapReduce
Lecture 48 - K-Machine Model (aka Pregel Model)
