Artificial Intelligence (AI) in Undergraduate Curriculum

In their future careers, students may work in areas that use or develop artificial intelligence and machine learning (AI/ML) methods and techniques for data analysis, code generation, prediction, optimization, and control – such as in the areas of robotics, self-driving cars, industrial automation, predictive maintenance, and smart grid management. At the FAMU-FSU College of Engineering, we believe it is necessary to equip our undergraduate students with the knowledge to use AI tools and methods for success in industry and delivering the next generation of solutions.

The AI@JCOE Task Force

In this initiative, we define AI as: The application and use of AI tools through programming interfaces provided by MATLAB, Python etc., not necessarily the core foundational mathematics embedded in the AI themselves. For example, this can include the following: using AI tools to aid in the completion of homework, quizzes, tests, and projects; teaching students responsible use of AI; exposing or using AI tools that are used in industry; using AI for data analysis or code generation; using AI for prediction, optimization, and control; teaching how AI tools work; developing AI tools; etc.

The AI@JCOE (Artificial Intelligence at the Joint College of Engineering) Task Force was charged with integrating artificial intelligence into our undergraduate engineering curricula. To accomplish the charge, the task force:

Met several times during spring 2025 semester
Considered four instructional delivery methods (options) for integrating AI into the curriculum
Conducted a survey of faculty at the college
Sought input from departmental advisory boards
Gathered lists of departmental courses that cover AI/ML topics
Consolidated the courses into a single list, which will be sent to departments for review and consideration of allowing the courses to meet elective requirements for their degrees

The AI@JCOE Task Force concluded that leveraging our existing courses, and creating new courses where departments have interest in doing so will enable individual departments to decide what is best for their students, disciplinary needs, and career readiness. Departments will be able to maintain authority over their program requirements, and students will be able to fit AI/ML course(s) into their program of study without being burdened with additional credit hours or time to graduation.

The task force will ask each department to allow at least one course from the list to satisfy a technical elective requirement for their undergraduate degree programs. We will monitor student interest in the AI courses each semester, especially noting cases where students take courses oﬀered by departments
other than their own. This data will inform the college about future developments or courses or seminars that could be created and oﬀered in area of artificial intelligence and machine learning.

Our course offerings in AI/ML at the college include:

EEL 4347 - Introduction to Cybersecurity (3)

Prerequisites: COP 3014 and EE: 3705.

This course is an introduction to computer security: symmetric ciphers, public-key cryptosystems, digital signatures, hashes, message authentication codes, key management and distribution, authentication protocols, vulnerabilities and malware, access control, and network security.

EEE 4773 - Machine Learning (3)

Prerequisites: EEL 3135, MAS 3105, knowledge of Matlab and/or Python, and instructor permission.

This course is designed for senior undergraduate students from engineering disciplines and introduces students to the theory and engineering applications of machine learning including neural networks, fuzzy logic, genetic algorithms, supervised and unsupervised learning algorithms. This course places emphasis on engineering applications in controls, power systems, and robotics.

EEE 4872 - Artificial Intelligence (3)

Prerequisites: COP 4530 and EEL 4021.

This course instructs students in basic artificial intelligence (AI) techniques of search, machine learning, natural language 6 of 12processing, robotics, and image processing. In this course, potential/current limitations are analyzed; as are human interaction in a decision-making environment.

EGN 4060 - Big Data Analytics in Engineering (3)

Prerequisite: EGN 3443.

This course introduces fundamentals of big data analytics, including data loading, cleaning, transformation, visualization, predictive analytics, and data-driven decision making. An emphasis is placed on computer implementation using state-of-the-art data analytics language.

EIN 4936 - Introduction to Data Engineering (3)

Prerequisites: This course is intended for undergraduate students who have taken COP 3014 Programming I - C++ or its equivalent.

Introductory course for undergraduate students to master high-level programming and database query languages for data extraction, transformation, and loading (ETL). Emphasis on utilizing computers to perform ETL operations relevant to industrial and systems engineering. Covers fundamentals of programming using a high-level language (such as Python) and introduces SQL for database management.

ESI 4234 - Quality Control and Reliability (3)

Prerequisite: EGN 3443.

This course is an introduction to quality and reliability engineering. This course examines statistical quality control techniques, process capability analysis, design and analysis of experiments for quality and reliability improvement.

ESI 4682 - Introduction to Machine Learning (3)

Prerequisites: EGN 3443, ESI 3312, and MAS 3105.

This course is an introduction to machine learning geared toward advanced undergraduates or first-year graduate students.

ESI 4686 - Deep Learning in Practice (3)

Prerequisites: EGN 3443, ESI 3312C, and MAS 3105.

This course introduces three main neural networks (ANN, CNN, and RNN) and the realization in Python. Students learn the basics such as forward propagation, backward propagation, and gradient descent algorithms, as well as up-to-date neural network projects like (YOLO, VGG19, Resnet50, etc.).

Foundational courses oﬀered at the college that could prepare students for later courses in machine learning include:

EEE 4510 - Digital Signal Processing (3)

Prerequisite: EEL 3135.

This course covers topics such as sinusoids, periodic signals, and Fourier spectra. Sampling of continuous-time signals, aliasing. Impulse response of linear, discrete-time systems, convolution. FIR filters and implementation. Frequency response of FIR filters. Z-transforms, IIR filters, poles and zeros, frequency response. Realization of IIR filters. Discrete Fourier transform and the FFT algorithm. MATLAB exercises are assigned.

EEL 4759 - Digital Image Processing (3)

Prerequisite: MAP 2302.

This course is an introduction to image processing techniques, including theoretical development, analysis, and practical implementation. A project that includes implementation grounds the successful student in current engineering practice.