I have been regularly teaching in the emphasis area of Signal Processing and Machine Learning. Following are the classes I have taught at Mississippi State University since 2018.
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ECE 4990/6990 Mathematical Foundations of Machine Learning
Course Offered: Spring 2020 , Spring 2021 (Designed and offered first time at MSU) Brief Course Detail: It is very important for engineering graduates to learn, apply and develop the necessary tools and fundamental background in data science, machine learning and artificial intelligence areas. Currently the general machine learning tools are given to the students as black boxes where the students learn to apply the tool in a given application using predefined settings or functions in programming environments. Although this is important, engineering students should have also solid mathematical knowledge and foundations of learning techniques to understand the basics of machine learning tools, extend and develop new techniques in this area rather than only being able to use the tools provided in different programming environments. The purpose of this course is to provide senior, masters or first year PhD students in engineering and computing with a solid mathematical background of modern data science in linear algebra. signal processing and applied probability. Mathematical background of both supervised and unsupervised machine learning will be introduced. Syllabus |
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ECE 8433 Statistical Signal Processing
Course Offered: Spring 2019 Brief Course Detail: The primary goal of this course is to introduce graduate students to the mathematical ideas that form the basis for modern statistically-based analysis of signals and systems. These methods are used in a wide range of engineering applications and form the fundementals of many current machine learning and deep learning approaches. Students will understand the fundementals tasks such as detection, classification and estimation with the underlying statistical/mathematical properties. Syllabus |
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ECE 3443 Signals and Systems
Course Offered: Spring 2019, Fall 2020, Spring 2022, Fall 2022, Fall 2023 Brief Course Detail: The objectives of this course are to introduce students to the basic concepts of signals, system modeling, and system classification; to develop students’ understanding of time-domain and frequency domain approaches to the analysis of continuous and discrete systems; to provide students with necessary tools and techniques to analyze systems and data; and to develop students’ ability to apply modern simulation software to system analysis. Syllabus |
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ECE 3313 Electromagnetics I
Course Offered: Fall 2018 Brief Course Detail: The objective of this course is to teach the fundemental laws and concepts governing the electromagnetic principles in the world. Students will understand important conceptd including Static and dynamic electromagnetic (EM) fields, energy, and power, EM fields and waves within and at the boundaries of media, EM radiation and propagation in space and within transmission lines. Syllabus |
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ECE 4433/6433 Introduction to Radar
Course Offered: Fall 2021 Brief Course Detail: Introduction to basic principles of radar and key radar sub-systems; radar range equation both point target and distributed forms. Radar cross section, beam-limited and pulse-limited clutter. radar measurements of range and velocity, basic radar waveforms, matched filtering, pulse compression, stretch processing. Radar waveform ambiguity functions, coded waveforms. Doppler processing, MTI filtering. Basic principles of radar target detection. Syllabus |
Additional Classes Offered:
- Smart Farming: Data enabled Agriculture (FYE 1001-F40): I have created this freshmen year experience class, with collaborating two instructors in agriculture departments to introduce data science to freshmen students in the context of agriculture applications. This was a 1 credit hour class, and I offered this class in addition to my normal teaching load.
- Directed Individual Studies (DIS - ECE 7000): I have taught a total of 12 DIS classes. These are specialized graduate-level classes for one student.
CAPSTONE Project Advising:
- Jacob Moore, Justin Herndon, Vignesh Raja, Scott Hoerchler, Harrison Welch, “The Land Ordnance Termination Unmanned System (L.O.T.U.S.)”, 2022
- Katherine Ardoin, Wes Hamlin, Brad Hamlin, Logan Johnson, Tyler Trege, “Beatwave: Design of a low cost EEG Device”, 2022
- Leasha Godbolt, S. Yasin, L. Anthony, W. Rashad, T. Woodbery, “OvenMax”, 2021
- Gabe Wiggins, Bruce Hicks, Braden Duke, Brandon Heron, “Flying Livestock Inventory Registrar” 2019
- Ben Bartlett, T. Phung, T. Welch, W. Wheeler, “PlantBot” 2019
- M. Russell, K. Splillers, A. Tew, L. Baioni, “LYRA - The Proactive Forklift Safety System - Improving Workplace Welfare” 2019
- K. Marcrum, B. Hartley, N. Johnson, J. Lang, J. Nguyen, “Cornhole” 2018
Teaching prior to MSU:
Taught Undergraduate classes at TOBB University (Turkey):
Taught Graduate classes at TOBB University (Turkey):
Taught Undergraduate classes at TOBB University (Turkey):
- ELE 201 Circuit Analysis I
- ELE201LCircuit Analysis I Lab
- ELE 202 Circuit Analysis II
- ELE 371 Signals and Systems
- ELE 474 Digital Signal Processing
- ELE 480 Introduction to Estimation
- ELE 495 Undergraduate Project
Taught Graduate classes at TOBB University (Turkey):
- ELE 465/565 Fundamentals of Radar Signal Processing,
- ELE 571 Detection and Estimation (established and offered first time at the university)
- ELE 670 Radar Signal Processing (established and offered first time at the university)
- ELE 675 Array Signal Processing (established and offered first time at the university)
- ELE 576 Special Problems
