Courses

E2.202

Random Processes

Basic mathematical modeling is at the heart of engineering. In both electrical and computer engineering, uncertainty can be modeled by appropriate probabilistic objects. This foundational course will introduce students to basics of probability theory, random variables, and random sequences.

Fall 2017--2022

E2.204

Stochastic Processes & Queueuing Theory

This course will introduce students to basic stochastic processes tools that can be utilized for performance analysis and stochastic modeling of dynamic systems and networks.

Spring 2015--2018, 2020, 2022--2023

E2.236

Foundations of Machine Learning

This course provides performance guarantees on various classes of machine learning algorithms.

Fall 2018--2019, Spring 2021--2022

E2.334

Topics in Computation over Networks

A large number of local microscopic interactions can lead to many interesting macroscopic physical phenomena. These effects have been observed in physical systems, and statistical physics presents models that can describe such effects. In this course, we will learn the techniques from statistical physics to describe complex network behaviors.

Spring 2019, 2021

E0.201

Proofs & Measures

This course is designed to enable engineering graduate students to learn basic mathematics for pursuing graduate level courses in mathematics. This course will provide the foundations in logic, introduction to topological and metric spaces, and probability measure.

Fall 2015

E1.244

Estimation & Detection Theory

This course is designed to enable engineering graduate students to learn basic estimation and detection theory applied to communications and signal processing.

Spring 2016--2017