Curriculum for AI Graduate Certificate

[Pending final approval of Faculty Senate; Program launches Fall 2025]

At the end of the three courses, students will have gained the foundational skills needed to effectively enter the field of AI Engineering, with a wide range of applications ranging from manufacturing, materials engineering, supply chain and logistics, healthcare systems, bioinformatics, chemical engineering, sustainable infrastructure planning and climate change adaption, etc. 

For students who choose to, the preparation will allow them to explore more advanced topics through independent research or advanced curricula. All courses will be taught through an engineering problem solving lens.

Current graduate students in the College of Engineering and non-matriculating students with undergraduate degrees, and sufficient technical background, are eligible to apply.

Read more about the AI Engineering Graduate Certificate and the overarching AI Engineering Program.

Requirements: 

This certificate is formulated and sequenced to provide the foundation and structure (Core courses 1 and 2), and in-depth focus on specialized topics (Elective), while at the same time being flexible to cater to different student interests (multiple specialization categories for Elective). 

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. 

Departments will collaborate to offer at least one course in Core 1, one in Core 2, and three in Electives every year.

(C1) Core Course 1 – Statistical Machine Learning for Engineers 

(pick ONLY one)

  • CEE 590ST Machine Learning Foundations and Applications
  • MIE 622 Predictive Analytics and Statistical Learning 

These courses focus on statistical machine learning methods that will help students understand the fundamentals of the machine learning field and use software packages to solve problems.

(C2) Core Course 2 – Deep Learning for Engineers 

(pick ONLY one)

  • ECE 601: Machine Learning for Engineers
  • CEE 616: Probabilistic Machine Learning

These courses focus on deep learning, including topics such as artificial neural networks, convolution neural networks, recurrent neural networks, auto encoders, and attention networks. These topics are foundational to AI algorithms.

Elective 

(pick AT LEAST one from any of the specialization categories) 

The Elective stream is categorized into specialization topics to guide students to choose an elective that most closely aligns with their career interests.

AI/ML Methods 

  • CEE 790ST: 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 

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)