Current courses

Throughout the course, we put the mathematical concepts into action with large-scale applications from machine learning, signal processing, and statistics.

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.

This course describes theory and methods for decision making under uncertainty under partial feedback.

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

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.

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.

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.

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.