NOC:Algorithms for Big Data


Lecture 1 - Lesson 1 - Basic definitions


Lecture 2 - Lesson 2 - Conditional probability


Lecture 3 - Lesson 3 - Example problems


Lecture 4 - Lesson 4 - Karger's mincut algorithm


Lecture 5 - Lesson 5 - Analysis of Karger's mincut algorithm


Lecture 6 - Lesson 6 - Random variables


Lecture 7 - Lesson 7 - Randomized quicksort


Lecture 8 - Problem solving video - The rich get richer


Lecture 9 - Problem solving video - Monty Hall problem


Lecture 10 - Lesson 1 - Bernoulli, Binomial, and Geometric distributions


Lecture 11 - Lesson 2 - Tail Bounds


Lecture 12 - Lesson 3 - Application of Chernoff bound


Lecture 13 - Lesson 4 - Application of Chebyshev's inequality


Lecture 14 - Lesson 1 - Intro to Big Data Algorithms


Lecture 15 - Lesson 2 - SAT Problem


Lecture 16 - Lesson 3 - Classification of States


Lecture 17 - Lesson 4 - Stationary Distribution of a Markov Chain


Lecture 18 - Lesson 5 - Celebrities Case Study


Lecture 19 - Lesson 6 - Random Walks on Undirected Graphs


Lecture 20 - Lesson 7 - Intro to Streaming, Morris Algorithm


Lecture 21 - Lesson 8 - Reservoir Sampling


Lecture 22 - Lesson 9 - Approximate Median


Lecture 23 - Lesson 1 - Overview


Lecture 24 - Lesson 2 - Balls, bins, hashing


Lecture 25 - Lesson 3 - Chain hashing, SUHA, Power of Two choices


Lecture 26 - Lesson 4 - Bloom filter


Lecture 27 - Lesson 5 - Pairwise independence


Lecture 28 - Lesson 6 - Estimating expectation of continuous function


Lecture 29 - Lesson 1 - Universal hash functions


Lecture 30 - Lesson 2 - Perfect hashing


Lecture 31 - Lesson 3 - 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 - Lesson 1 - Estimating frequency moments


Lecture 37 - Lesson 2 - Property testing framework


Lecture 38 - Lesson 3 - Testing Connectivity


Lecture 39 - Lesson 4 - Enforce & Test Introduction


Lecture 40 - Lesson 5 - Testing if a graph is a biclique


Lecture 41 - Lesson 6 - Testing bipartiteness


Lecture 42 - Lesson 1 - Property testing and random walk algorithms


Lecture 43 - Lesson 2 - Testing if a graph is bipartite (using random walks)


Lecture 44 - Lesson 3 - Graph streaming algorithms : Introduction


Lecture 45 - Lesson 4 - Graph streaming algorithms : Matching


Lecture 46 - Lesson 5 - Graph streaming algorithms : Graph sparsification


Lecture 47 - Lesson 1 - MapReduce


Lecture 48 - Lesson 2 - K-Machine Model (aka Pregel Model)