Videos
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)
PDF
NPTEL PDF Lecture links are currently unavailable for this video course
All the NPTEL Video and PDF lectures are available for free download from the NPTEL website https://nptel.ac.in
NPTEL Video Course : NOC:Algorithms for Big Data
Lecture 13 - Application of Chebyshev's inequality
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