# ECE 498MR: Introduction to Stochastic Systems

(Spring 2017)

##
Maxim Raginsky (contact: `maxim [at] illinois [dot] edu`)

TA: Jaeho Lee (contact: `jlee620 [at] illinois [dot] edu`)

MW 12:30-1:50, 3013 ECE Building

### Announcements

- Mar 6: correction in Problem 1, Homework 3.
- Mar 1: Homework 3 is posted, due at the beginning of class on Mar 8.
- Mar 1: notes for Lecture 12 (Campbell's theorem) are posted.
- Feb 27: Programming Assignment 1 is released, due by the end of Mar 13. See the Coursework section for instructions.
- Feb 27: notes for Lectures 10-11 (including Bussgang's theorem) are posted.
- Feb 20: notes for Lecture 9 are posted.
- In-class midterm exam will be on Wednesday, Mar 15.
- Feb 14: notes for Lecture 8 (on the Poisson process) are posted.
- There will be no instructor office hours on Monday, Feb 13. Instead, there will be extra TA office hours on Tuesday, Feb 14, from 3pm to 5pm in 146 CSL.
- Homework 2 is posted, due at the beginning of class on Feb 15.
- Feb 7: notes for Lectures 6-7 (up to and including the Wiener process) are updated.
- Feb 7: notes for Lectures 3-6 are updated.
- Note the change in TA office hours: Fridays, 3-5pm, 146 CSL.
- Homework 1 is posted in the Coursework section, due at the beginning of class on Feb 1.
- Office hours:
- instructor -- Mondays, 2-3pm, 162 CSL
- TA -- Fridays, 3-5pm, 146 CSL

- Watch this space for all course-related announcements.

### About this class

ECE 498MR: Introduction to Stochastic Systems is a senior undergraduate course dedicated to exploration of noise, uncertainty, and randomness in the context of signals and systems. The course will introduce discrete- and continuous-time random processes as input and/or output signals of various types of systems, with and without memory or feedback. Probabilistic notions will be tightly integrated with techniques from signals and systems, such as linearity, time-invariance, causality, transform methods, and stability. Basic concepts will be illustrated via numerous examples, such as noise in linear and nonlinear circuits, average consensus and PageRank, queuing systems, noise in remote sensing applications, Bayesian filtering, Monte Carlo simulation, risk allocation in financial portfolios, stochastic gradient descent.
For more details, consult the course syllabus.