Mathematics of Big Data and Machine Learning, IAP 2020

Overview

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Explore the mathematics behind big data and machine learning in this 14-hour course from MIT. Delve into artificial intelligence with a focus on data handling challenges, guided by instructors Jeremy Kepner and Vijay Gadepally. Learn about cyber network data processing, AI data architecture, and the use of associative arrays. Discover group theory applications, entity analysis in unstructured data, and structured data analysis techniques. Investigate perfect power law graphs, bio sequence cross-correlation, and Kronecker graphs. Gain hands-on experience through numerous demonstrations accompanying each lecture topic. Enhance your understanding of the mathematical foundations crucial for tackling complex data and AI challenges in this comprehensive IAP class.

Syllabus

1. Artificial Intelligence and Machine Learning.
2. Cyber Network Data Processing; AI Data Architecture.
Lecture: Mathematics of Big Data and Machine Learning.
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.