Machine Learning Certificate Program
Curriculum
In order to receive the certificate, students must complete four courses, equivalent to 12 credits, maintaining a GPA of at least 3.0. This coursework must include a minimum of two courses from List A, and a maximum of two courses from List B:
List A
• 14:332:443 Machine Learning for Engineers (or its graduate-level equivalent course)
• 16:332:515 Reinforcement Learning for Engineers
• 16:332:530 Introduction to Deep Learning
• 16:332:549 Detection & Estimation Theory: Inference & Machine Learning for Engineers
• 16:332:561 Machine Vision
List B
• 16:332:509 Convex Optimization
• 16:332:518 Mobile Embedded Systems and On-Device AI
• 16:332:525 Optimum Signal Processing
• 16:332:531 Probabilistic Methods for Large Scale Signal Processing and Learning
• 16:332:532 Multimodal Machine Learning for Sensing Systems
• 16:332:533 Machine Learning for Inverse Problems
We understand that the landscape of Machine Learning is dynamic, and therefore our graduate curriculum is updated regularly to keep pace with the latest advancements. In light of this, additional courses beyond those mentioned above could be accepted towards the completion of this certificate, subject to the sole discretion of the ECE Graduate Program Director and subsequent approval from the School of Graduate Studies. This ensures our program remains flexible, current, and responsive to the evolving needs of the field.