Teaching

Courses Recently Taught

EECS 203: Discrete Mathematics

**Prerequisites:** MATH 115 **Course Description:** Introduction to the mathematical foundations of computer science. Topics covered include: propositional and predicate logic, set theory, function and relations, growth of functions and asymptotic notation, introduction to algorithms, elementary combinatorics and graph theory and discrete probability theory.

EECS 216: Introduction to Signals and Systems

**Prerequisites:** EECS 215; Preceded or accompanied by MATH 216 **Course Description:** Theory and practice of signals and systems engineering in continuous and discrete time. Continuous-time linear time-invariant systems, impulse response, convolution. Fourier series, Fourier transforms, spectrum, frequency response and filtering. Sampling leading to basic digital signal processing using the discrete-time Fourier and the discrete Fourier transform. Laplace transforms, transfer functions, poles and zeros, stability. Applications of Laplace transform theory to RLC circuit analysis. Introduction to communications, control, and signal processing. Weekly recitations and hardware/Matlab software laboratories.

EECS 298: From 51 Billion to Zero: Challenges and Opportunities for Reducing Greenhouse Emissions

Through a series of recorded interviews, learn how Electrical and Computer Engineers affiliated with the University of Michigan are helping to reduce greenhouse gas emissions in their own ways.

Watch the entire video series, recorded in 2023.

EECS 498: Special Topics: Introduction to Discrete-Event and Hybrid Systems

**Prerequisites:** Senior or Graduate standing **Course Description:** This course was offered in Fall 2007 and Winter 2010.

ECS 566: Discrete Event Systems

[Catalog Description](https://ece.engin.umich.edu/academics/course-information/course-descriptions/eecs-566/) Fall 2022 Information Flyer: [Media:Poster-EECS566-F22.pdf](attachments/poster-eecs566-f22.pdf) **Prerequisites:** Graduate standing or permission of instructor **Course Description:** Modeling, analysis, and control of discrete event systems; untimed (logical) and timed models considered. Defining characteristics of discrete event systems. Logical models: languages, automata, and Petri nets. Analysis: safety, nonblocking, state estimation, and event diagnosis. Supervisory control: controllability, nonblocking and nonconflicting languages, observability, and coobservability. Control of Petri nets using place invariants. Timed models: timed automata and timed Petri nets; timed automata with guards. Brief introduction to stochastic models.