The Graduate Certificate in Signal Image Processing and Machine Learning (SIPML) allows current industry employees to advance their skills in a highly sought out focus, while maintaining their full-time employment.

Due to the evolution of engineering fields, especially with the introduction of artificial intelligence, large language models, and ever-changing coding practices, there may be a skill gap between current and past graduates working in industry. To fill this gap, the SIPML Certificate allows current industry employees the opportunity to take courses on a part time basis to refresh and broaden their knowledge.

The core courses set the foundation for certificate students, while the elective courses allow students the flexibility to enhance the knowledge most relevant for their specific sector of industry.

Program Overview

The SIPML Certificate Consists of 15 total credits of coursework, broken down into Core Curriculum and Electives. Appendix A provides descriptions of the courses. This certificate is currently intended to be an in-person learning program.

Core Curriculum (choose at least 2/3)
ECE 501 – (4 cr.) Probability and Random Processes
ECE 551 – (4 cr.) Matrix Methods for Sig. Proc., Data Analysis and Machine Learning
EECS 553 – Machine Learning (ECE)

Electives
ECE 516 – Medical Image Systems
ECE 556 – Image Processing
ECE 559 – Optimization methods for SIPML
ECE 564 – Estimation and detection
EECS 442 – (4 cr.) Computer vision (take at most one of 442 or 504)
EECS 504 – Foundations of Computer Vision
EECS 542 – Advanced Topics in Computer Vision
ECE 598 – Special topics (with SIPML Graduate Advisor Approval)

Courses taken for this certificate program can be applied to the Data/Science /Machine Learning (DS/ML) MEng program, provided a student applies to the MEng program prior to completion of the certificate program and is admitted. In this case, the certificate will be discontinued when the student meets the MEng degree requirements and applies for graduation. Courses taken as part of the certificate prior to the MEng application will be eligible for the MEng degree to allow for a seamless transition into the MEng program.