IAP 2021 Activity: Mathematics of Big Data & Machine Learning

Mathematics of Big Data & Machine Learning

Jeremy Kepner, Fellow & Head MIT Supercomputing Center

Enrollment: Limited: Advance sign-up required
Limited to 30 participants
Attendance: Participants must attend all sessions
Prereq: Matrix Mathematics

Big Data describes a new era in the digital age where the volume, velocity, and variety of data created across a wide range of fields is increasing at a rate well beyond our ability to analyze the data. Machine Learning has emerged as a powerful tool for transforming this data into usable information. Many technologies (e.g., spreadsheets, databases, graphs, matrices, deep neural networks, ...) have been developed to address these challenges. The common theme amongst these technologies is the need to store and operate on data as tabular collections instead of as individual data elements. This class describes the common mathematical foundation of these tabular collections (associative arrays) that apply across a wide range of applications and technologies. Associative arrays unify and simplify Big Data and Machine Learning. Understanding these mathematical foundations allows the student to see past the differences that lie on the surface of Big Data and Machine Learning applications and technologies and leverage their core mathematical similarities to solve the hardest Big Data and Machine Learning challenges.

This interactive course will involve significant interactive student participation and a small amount of homework. Those students who fully participate and complete the homework will receive a certificate of completion.

The MIT Press book "Mathematics of Big Data" that willl be used throughout the course will be provided.

E-mail the instructor to sign up.

Sponsor(s): Mathematics
Contact: Jeremy Kepner, MIT Beaver Works (300 Tech Sq), 781 981-3108, KEPNER@LL.MIT.EDU