Courses
Current courses
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-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.
Past courses
EE-618 Theory and Methods for Reinforcement Learning
This course describes theory and methods for decision making under uncertainty under partial feedback.
EE-204 Circuits and Systems I
This course offers an elementary introduction to signals and systems. Our goal is to help students understand mathematical descriptions of signal processing algorithms and express those algorithms as basic computer implementations via MATLAB.
EE-614 Theory and Methods for Linear Inverse Problems
This course is about inference from incomplete data in high-dimensional linear systems. The core topics will revolve around the following concepts: Foundations of low dimensional models, such as sparsity and low-rank models, Convex geometry in high dimensions, Randomness in high dimensions Convex and combinatorial optimization, Analysis and design of algorithms.
EE-717 Graphical Models
The course focuses on providing diverse mathematical tools for graduate students from statistical inference and learning; graph theory, signal processing and systems; coding theory and communications, and information theory.
EE-731 Advanced Topics in Data Sciences
This course describes theory and methods to address three key challenges in data sciences: estimation, prediction, and computation. We use convex analysis and methods as a common connecting theme, and illustrate the main ideas on concrete applications from machine learning and signal processing.