Jeremy Kepner talked about his newly released book, "Mathematics of Big Data," which serves as the motivational material for the D4M course.

Lecture: Mathematics of Big Data and Machine Learning

Introduction to signal processing applied to graphs. Course outline. Discussion of relevant technologies programming and storage technologies. Constructing a graph from raw data.

Lecture 0: Introduction

D4M.mit.edu software demo example/1Intro/2EdgeArt. Adjacency matrix construction. Incidence matrix construction.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/1Intro/2EdgeArt. Adjacency matrix construction. Incidence matrix construction.

Lecture 1: Examples Demonstration

Historical evolution of the web and cloud computing. Using the exploded (D4M) schema. Analyzing computer network data. Analyzing computer network data.

Lecture 3: Entity Analysis in Unstructured Data

Computing statistics and analytics on data in the exploded (D4M) schema.

Lecture 4: Analysis of Structured Data

D4M.mit.edu software demo examples/2Apps/4BioBlast. Distribution of genetic sample data. Ingesting genetic data into an associative array using the exploded (D4M) schema. Correlating genetic data via associative array multiplication.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/2Apps/4BioBlast. Distribution of genetic sample data. Ingesting genetic data into an associative array using the exploded (D4M) schema. Correlating genetic data via associative array multiplication.

Lecture 6: Examples Demonstration

D4M.mit.edu software demo example/2Apps/1EntityAnalysis. Incidence array of text entities. Computing the entity degree distribution.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/2Apps/1EntityAnalysis. Incidence array of text entities. Computing the entity degree distribution.

Lecture 3: Examples Demonstration

D4M.mit.edu software demo examples/3Scaling/1KroneckerGraph. Generation of power law graphs via Kronecker products. D4M.mit.edu software demoexamples/3Scaling/3MatrixPerformance. Measuring the performance of array multiplication.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/3Scaling/1KroneckerGraph. Generation of power law graphs via Kronecker products. <a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demoexamples/3Scaling/3MatrixPerformance. Measuring the performance of array multiplication.

Lecture 7: Examples Demonstration

D4M.mit.edu software demo examples/3Scaling/2ParallelDatabase. MIT SuperCloud database management system. Starting the Apache Accummulo database. High performance database ingest. Using the D4M schema.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/3Scaling/2ParallelDatabase. MIT SuperCloud database management system. Starting the Apache Accummulo database. High performance database ingest. Using the D4M schema.

Demonstration 7

Genectic sequence analysis using associative arrays. Creating of a genetic processing pipeline. Ingest of genetic sample data into a database. Sub-sampling of data. Correlation of genetic samples using associative array multiplication.

Lecture 6: Bio Sequence Cross Correlation

D4M: Signal Processing on Databases

Associative array mathematics. Relevant operations on an associative array. Semirings and matrices. See MIT Press book "Mathematics of Big Data."

Lecture 2: Group Theory

Theory of Kronecker graphs. Database ingest performance and database query performance. Array multiplication performance.

Lecture 7: Kronecker Graphs, Data Generation, and Performance

D4M.mit.edu software demo example/1Intro/3GroupTheory. Associativity, commutativity, distributivity properties.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/1Intro/3GroupTheory. Associativity, commutativity, distributivity properties.

Lecture 2: Examples Demonstration

D4M.mit.edu software demo examples/2Apps/3PerfectPowerLaw. Generate synthetic power law distributions.  Analyze power law distributions in real data.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/2Apps/3PerfectPowerLaw. Generate synthetic power law distributions.  Analyze power law distributions in real data.

Lecture 5: Examples Demonstration

Statistical distribution of background/noise in databases. Power law distribution describes many backgrounds. Perfect power law distribution can be used to bin and model the background data.

Lecture 5: Perfect Power Law Graphs -- Generation, Sampling, Construction, and Fitting

The best podcast episodes of anyone with an interesting story. With famous guests like Obama, LeBron James, Drake, Oprah Winfrey, Richard Branson, Michelle Obama, Serena Williams, to anyone who has a unique and interesting story to tell. Add episodes you're interested into your queue below!

Best Episodes: Personal Stories & Great Interviews

The best podcast episodes of the most interesting stories and things. From skyping someone while they're in space, to tracking down and meeting a telephone scammer in person, to the story of a Galapagos. These episodes will keep you and all your friends entertained and captivated for hours. Learn something new in every episode! Add episodes you're interested into your queue below!