EE-556 Mathematics of Data: From Theory to Computation
Throughout the course, we put the mathematical concepts into action with large-scale applications from machine learning, signal processing, and statistics.
EE-568 Reinforcement Learning
This course describes theory and methods for Reinforcement Learning (RL), which revolves around decision making under uncertainty. The course covers classic algorithms in RL as well as recent algorithms under the lens of contemporary optimization.
EE-628 Training Large Language Models
This PhD-level course dives deep into the training of Large Language Models (LLMs), focusing on the complementary roles of datasets, pre-training and post training methodologies in shaping model performance and scalability.
EE-735 Online learning in games
This course provides an overview of recent developments in online learning, game theory, and variational inequalities and their point of intersection with a focus on algorithmic development. The primary approach is to lay out the different problem classes and their associated optimal rates.