Course Content
MIT's course RES.LL-005, titled "Mathematics of Big Data and Machine Learning," was offered during the Independent Activities Period (IAP) in January 2020. Instructed by Dr. Jeremy Kepner and Dr. Vijay Gadepally, the course introduces the Dynamic Distributed Dimensional Data Model (D4M), a computational approach that integrates graph theory, linear algebra, and database design to tackle challenges associated with big data.
Course Content:
The curriculum emphasizes a signal processing perspective on big data problems, covering topics such as:
- Artificial Intelligence and Machine Learning
- Cyber Network Data Processing
- AI Data Architecture
- D4M (Dynamic Distributed Dimensional Data Model)
Students engage with practical problems, delve into the relevant theory, and apply these concepts through assignments and a final project of their choosing.
Course Materials:
MIT OpenCourseWare provides comprehensive resources for this course, including:
- Lecture videos
- Lecture notes
- Instructor insights
These materials are accessible for self-paced learning.
Additional Resources:
For a structured viewing experience, a playlist of the course lectures is available on YouTube:
MIT RES.LL-005 Mathematics of Big Data and Machine Learning, IAP 2020
This course is ideal for individuals interested in the mathematical foundations of big data and machine learning, offering both theoretical insights and practical applications.