Teaching
Teaching in the IMPRESS Lab centres on the signal processing and machine learning emphasis area — from the core undergraduate mathematics that engineering rests on, through signals and systems, to graduate electives in radar and array processing that lead directly into the lab’s research.
At NC State University (2025– )
ECE 220 — Analytical Foundations for ECE
Offered: Spring 2025, Spring 2026
The core mathematical foundations for electrical and computer engineering: complex numbers and the complex exponential, linear algebra, and differential equations — the analytical language behind circuits, signals, systems, and machine learning.
ECE 301 — Linear Systems
Offered: Fall 2025
Representation and analysis of signals and linear time-invariant systems in continuous and discrete time: convolution, Fourier series and transforms, sampling, and transform-domain analysis — the foundation of modern signal processing.
ECE 792 — Radar and Array Signal Processing
Offered: Fall 2026
A graduate course covering the principles of radar and sensor array processing: the radar range equation and radar cross section, matched filtering and pulse compression, ambiguity functions and waveform design, Doppler processing and clutter, array geometries and beamforming, direction-of-arrival estimation, adaptive and space-time adaptive processing, and modern learning-based approaches to radar and array problems.
At Mississippi State University (2018–2024)
ECE 4990/6990 — Mathematical Foundations of Machine Learning
Offered: Spring 2020, Spring 2021 — designed and offered for the first time at MSU
Gives senior, masters and first-year Ph.D. students in engineering and computing a solid mathematical background in the linear algebra, signal processing and applied probability behind modern data science, covering the foundations of both supervised and unsupervised learning — so that students can understand, extend and develop learning techniques rather than only applying black-box tools.
ECE 8433 — Statistical Signal Processing
Offered: Spring 2019
Introduces graduate students to the mathematical ideas behind modern statistically-based analysis of signals and systems: detection, classification and estimation together with their underlying statistical properties — the foundations on which many current machine learning and deep learning methods are built.
ECE 3443 — Signals and Systems
Offered: Spring 2019, Fall 2020, Spring 2022, Fall 2022, Fall 2023
Basic concepts of signals, system modelling and system classification; time-domain and frequency-domain approaches to the analysis of continuous and discrete systems, with modern simulation software.
ECE 4433/6433 — Introduction to Radar
Offered: Fall 2021
Basic principles of radar and its key sub-systems: the radar range equation, radar cross section, clutter, measurement of range and velocity, waveforms, matched filtering, pulse compression and stretch processing, ambiguity functions and coded waveforms, Doppler processing and MTI filtering, and the principles of radar target detection.
ECE 3313 — Electromagnetics I
Offered: Fall 2018
Fundamental laws and concepts governing electromagnetics: static and dynamic fields, energy and power, fields and waves within and at the boundaries of media, and radiation and propagation in space and within transmission lines.
Additional teaching at MSU
- Smart Farming: Data-Enabled Agriculture (FYE 1001) — a freshman-year experience course created with two collaborating instructors in agriculture departments, introducing data science to first-year students in the context of agricultural applications.
- Directed Individual Studies (ECE 7000) — twelve specialised graduate-level courses taught one-on-one.
- Senior capstone advising — including “L.O.T.U.S.: Land Ordnance Termination Unmanned System” and “Beatwave: Design of a Low-Cost EEG Device” (2022), “OvenMax” (2021), “Flying Livestock Inventory Registrar”, “PlantBot” and “LYRA: Proactive Forklift Safety System” (2019).
For courses taught before 2018, including undergraduate and graduate courses at TOBB University of Economics and Technology, see the teaching page on Dr. Gurbuz’s personal website.