# InfoCoBuild

## Algorithms for Big Data

Algorithms for Big Data. Instructor: Prof. John Augustine, Department of Computer Science and Engineering, IIT Madras. In this course, you will learn how to design and analyse algorithms in the streaming and property testing models of computation. The algorithms will be analysed mathematically, so it is intended for a mathematically mature audience with prior knowledge of algorithm design and basic probability theory.

Traditional algorithms work well when the input data fits entirely within memory. In many modern application contexts, however, the size of the input data is too large to fit within memory. In some cases, data is stored in large data centres or clouds and specific parts of it can be accessed via queries. In some other application contexts, very large volume of data may stream through a computer one item at a time. So the algorithm will get to see the data typically as a single pass, but will not be able to store the data for future reference. In this course, we will introduce computational models, algorithms and analysis techniques aimed at addressing such big data contexts. (from nptel.ac.in)

 Lecture 12 - Application of the Chernoff Bound

Go to the Course Home or watch other lectures:

 Lecture 01 - Basic Definitions: Basics of Probability Theory Lecture 02 - Conditional Probability Lecture 03 - Examples - How to Use Probability to Solve Problems Lecture 04 - Karger's Mincut Algorithm Lecture 05 - Analysis of Karger's Mincut Algorithm Lecture 06 - Random Variables Lecture 07 - Randomized Quicksort Lecture 08 - Problem Solving Example - The Rich Get Richer Lecture 09 - Problem Solving Example - Monty Hall Problem Lecture 10 - Bernoulli, Binomial, and Geometric Distributions Lecture 11 - Tail Bounds Lecture 12 - Application of the Chernoff Bound Lecture 13 - Application of Chebyshev's inequality Lecture 14 - Introduction 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 - Introduction to Streaming, Morris Algorithm Lecture 21 - Reservoir Sampling Lecture 22 - Approximate Median Lecture 23 - Hashing and Pairwise Independence: 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 - Doubly Stochastic Transition Matrix Lecture 33 - Problem Solving - Random Walks on Linear Structures Lecture 34 - Problem Solving - Lollipop Graph Lecture 35 - Problem Solving - Cat and Mouse Lecture 36 - Estimating Frequency Moments Lecture 37 - Property Testing Framework Lecture 38 - Testing Connectivity Lecture 39 - Property Testing: The Enforce and Test Technique 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)