Program Overview

Engineering plays a unique and critical role in preparing a workforce skilled to meet the emerging demands of applying AI in real-world settings—bridging the gap between theoretical AI research and practical implementation.

The AI Engineering Graduate Certificate, a college-wide collaborative, is designed to meet the immediate and urgent demands of a transitioning industry landscape.

AI Engineering is a nascent field leveraging the traditional strengths of engineering disciplines with developments in artificial intelligence (AI). The term was coined by the NSF Engineering Research Visioning Alliance (ERVA), Report | AI Engineering: A Strategic Research Framework to Benefit Society As the report highlights “AI Engineering will be bidirectional and reciprocal: it evokes a future vision in which an engineering approach makes for better AI while AI makes for better-engineered systems." 

Engineering for AI integrates engineering techniques and domain knowledge across various disciplines to develop explainable and reliable AI-enabled systems. AI for Engineering, on the other hand, utilizes AI to enhance computational efficiency, automation, creativity, and scalability in engineered systems and processes.

Requirements:

To receive the certificate, students will take at least 9 credits at the 600-level or higher.  Students will take one course from C1, one course from C2, and one course from Elective (see below) categories.:

(C1) Core Course- 1 – Statistical Machine Learning for Engineers (pick ONLY one course):  

MIE 622: Predictive Analytics and Statistical Learning

CEE 601: Machine Learning Foundations and Applications

(C2) Core Course- 2 – Deep Learning for Engineers (pick ONLY one course):

ECE 601: Machine Learning for Engineers

CEE 616: Probabilistic Machine Learning

MIE 625: Deep Learning for Engineering Applications

Elective (pick ONLY one course from any of the below)

AI/ML Methods  

CEE790ST: Advanced Probabilistic Machine Learning

MIE 624: Machine Learning for Dynamic Decision-Making

Engineering Applications

BME 615: AI in Biomedicine

ECE 627: Artificial Intelligence Based Wireless Network Design

ECE 629: Applied Machine Learning for the Internet of Things

MIE 659: Intelligent Manufacturing

MIE 650: Vehicle Automation

Hardware Design

ECE 662: Hardware Design for Machine Learning Systems

ECE 676: Neuromorphic Engineering

Signal Processing

ECE 746: Statistical Signal Processing

ECE 608: Signal Theory

BME 609: Biomedical Signals and Systems

Students are required to take one course from C1 (core course 1) and one from C2 (core course 2). Though students can take a C1 and a C2 in the same semester, it is recommended that they do it sequentially; while they are separate topics, a C1 course can better prepare students for a C2 course. Students can take an Elective course after C1 if it does not need C2.

Prerequisites: Undergraduate level courses in the following. These courses are typical coursework of most undergraduate engineering programs. All but Linear Algebra are currently required of majors within the College of Engineering:

• Linear algebra
• Probability and statistics
• Multivariate calculus
• Programming (Python or R are typically used in the above courses; efficiency in programming to learn new packages or libraries would be necessary)