Curriculum for AI Graduate Certificate

Gain the foundational skills needed to effectively enter the field of AI Engineering. Read an overview of our AI Engineering Graduate Certificate.

How to apply:

Please fill out the Intention to Complete the AI Engineering Graduate Certificate form. It is mandatory to submit the form before enrolling in your first course for the AI Engineering Certificate.  You must be registered at UMass Amherst as a graduate student or a  non-degree graduate student before completing the intention form. Read the curriculum requirements including pre-requisites prior to submission. Acceptance into the certificate program does not automatically qualify a student for any of the MS or PhD programs in the college, although the student may attempt to join such programs at any time.

Have a question?

We are committed to providing you with tailored guidance and support throughout the duration of your certificate program. Please direct any questions to Nauman Tazeem at ntazeem [at] umass [dot] edu (ntazeem[at]umass[dot]edu).  

Receiving your certificate:

In the semester you are completing the final certificate course(s), you will need to submit the certificate eligibility form (only sections A and B) and email the completed form by the stated deadline to Nauman Tazeem. The timeline for receiving the certificate is same as that listed for the diploma, and the details can be found here.  

Curriculum/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 each from C1 (core course 1), C2 (core course 2) and Elective.  course after C1 if it does not need C2. It is recommended to take C1 and C2 sequentially as 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.

Statistical Machine Learning for Engineers  (pick ONLY one)
CEE 601 Machine Learning Foundations and Applications
MIE 622 Predictive Analytics and Statistical Learning 

Deep Learning for Engineers (pick ONLY one)
ECE 601: Machine Learning for Engineers
CEE 616: Probabilistic Machine Learning
MIE 690D: Deep Learning for Engineering Application

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 

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)