MEng in AI Curriculum
Curriculum Overview
Curriculum
The MEng program requires 12 course credits in AI/ML foundations, 12 credits in domain intersection in biomedicine (typically courses in BME), and 6 credits for Knowledge Extensions.
The Graduate Certificate Program requires 6 credits in AI/ML Fundamentals Core Courses and 6 credit hours of Domain Intersection Courses. Individualized plans of study will be developed by students in consultation with an advisor. Refer to the Engineering Course Guide and Bulletin for course descriptions. Students in the BME concentration for both the certificate and MEng programs are required to take at least two BME courses in the list below:
Sample BME concentration coursework includes:
- BIOMEDE 487: Artificial Intelligence in Biomedical Engineering
- BIOMEDE 503: Statistical Methods for Biomedical Engineering
- BIOMEDE 517: Sensing and Machine Learning for Neural Interfaces
- BIOMEDE 599: Deep Learning for Signal and Image Processing
- BIOMEDE 599.017: AI for Experiment Design (residential format only)
- EECS 598: Artificial Intelligence in Biomedicine
Students may also take foundational AI and machine learning courses outside the
BME department in areas such as matrix methods, optimization, reinforcement learning, and machine learning. The MEng program requires 12 credits in AI/ML
foundations. Recommended foundational courses include:
- EECS 545: Machine Learning
- CSE 592: Foundations of Artificial Intelligence
- EECS 551: Matrix methods in SIPML
- EECS 505: Computational Data Science & ML
- ECE 553: Machine Learning
- EECS 453: Principles of Machine Learning
- ECE 559: Optimization methods in SP and ML
Course Guide and Bulletin, Course List/Degree Requirements (Coming Soon)
All the courses listed above, except BIOMEDE 599.017, are available in both residential and online formats.