This course focuses on artificial intelligence and machine learning with an emphasis on data handling challenges. The learning outcomes include understanding the mathematics behind big data and machine learning, as well as gaining knowledge in areas such as associative arrays, group theory, entity analysis, structured data analysis, graph theory, bio sequence cross-correlation, and Kronecker graphs. The teaching method includes lectures and demonstrations. This course is intended for individuals interested in artificial intelligence, machine learning, and data handling challenges.
Mathematics of Big Data and Machine Learning, IAP 2020
Massachusetts Institute of Technology via YouTube
Overview
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.
Taught by
MIT open courseware
