Mathematical Aspects of Machine Learning

View the Project on GitHub jeremy9959/Math-5800-Spring-2020

Mathematical Aspects of Machine Learning

Jeremy Teitelbaum
University of Connecticut
Department of Mathematics
Math 5800, Spring Term 2020

Description: This is a project-based course in which students will explore both practical and mathematical problems arising in machine learning and data science. Possible topics include random walks for graph embeddings, optimization techniques, Monte Carlo methods, autoencoders, and linear and non-linear dimension reduction.

Course background

References and News


Note regarding COVID-19: In light of the COVID-19 pandemic, the balance of this course will be conducted online. The final project will continue to be a github repo or website. The university has announced changes to the pass/fail regulations and has waived the drop deadline. This class is already being offered pass/fail.

Notes from the first two weeks:

We will continue our practice so far: