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.
Notes from the first two weeks:
For the remainder of the course we will follow this outline:
Fridays: I will usually give a brief presentation on a topic in machine learning. So far I’ve discussed: