Graduate Certificate in Neural Engineering-Signals, Systems and Machine Learning

The Graduate Certificate in Neural Engineering–Signals, Systems and Machine Learning will enable the student to gain both fundamental and applied understanding of brain signals and systems, and machine learning schemes in this rapidly growing component of neural big-data research. The program includes the study of basic concepts related to modeling the nonlinear electrical circuits in the brain which use concepts from signal processing, systems modeling and control disciplines. The students will gain expertise in understanding the fundamentals of signals, systems and machine learning tools for "reverse engineering the brain", and also for the design of neural prostheses, and brain machine interfaces.


Students will need to complete 12 credit hours to earn the certificate. 

Required Courses (select two courses)*6
ECE 7540Neural Models and Machine Learning3
or CMP_SC 7540 Neural Models and Machine Learning
ECE 7310Feedback Control Systems3-4
or BIOL_EN 7310 Feedback Control Systems
or ECE 7830 Introduction to Digital Signal Processing
ECE 7590Computational Neuroscience4
or CMP_SC 7590 Computational Neuroscience
or BIOL_EN 7590 Computational Neuroscience
Support Courses6
Can select one of the 7000-level courses from above3
ECE 8810Advanced Digital Signal Processing3
ECE 8860Probability and Stochastic Processes for Engineers3
ECE 8570Neural Dynamics and Communication3
or CMP_SC 8570 Neural Dynamics and Communication
ECE 8580Machine Learning in Neuroscience3
or CMP_SC 8580 Machine Learning in Neuroscience

Core courses need to be taken before or parallel to the elective courses.