Mathematics of Big Data and Machine Learning
This course introduces the Dynamic Distributed Dimensional Data Model (D4M), a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in vast quantities of data. This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms, group theory, and database design. This approach has been implemented in software.
Topics taught in this course:
- Dynamic Distributed Dimensional Data Model (D4M)
- Graph theory
- Linear algebra
- Databases to address problems associated with Big Data
- Signal processing approach
- Combining linear algebraic graph algorithms, group theory, and database design
- Practical problems
- Applying the appropriate theory to problems
- Software infrastructure
- and more!
Instructors: Dr. Jeremy Kepner and Dr. Vijay Gadepally
Course provided by MIT OpenCourseWare
Lecture: Mathematics of Big Data and Machine Learning
Artificial Intelligence and Machine Learning
Cyber Network Data Processing; AI Data Architecture
0. Introduction
0. Examples Demonstration
1. Using Associative Arrays
1. Examples Demonstration
2. Group Theory
2. Examples Demonstration
3. Entity Analysis in Unstructured Data
3. Examples Demonstration
4. Analysis of Structured Data
4. Examples Demonstration
5. Perfect Power Law Graphs -- Generation, Sampling, Construction, and Fitting
5. Examples Demonstration
6. Bio Sequence Cross Correlation
6. Examples Demonstration
Demonstration 7
7. Kronecker Graphs, Data Generation, and Performance
7. Examples Demonstration
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