Graduate Certificate in Neural Engineering-Signals, Systems and Machine Learning
Begin Admitting Fall 2020.
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 7540||Neural Models and Machine Learning||3|
|or CMP_SC 7540||Neural Models and Machine Learning|
|ECE 7310||Feedback Control Systems||3-4|
|or BIOL_EN 7310||Feedback Control Systems|
|or ECE 7830||Introduction to Digital Signal Processing|
|ECE 7590||Computational Neuroscience||4|
|or CMP_SC 7590||Computational Neuroscience|
|or BIOL_EN 7590||Computational Neuroscience|
|Can select one of the 7000-level courses from above||3|
|ECE 8810||Advanced Digital Signal Processing||3|
|ECE 8860||Probability and Stochastic Processes for Engineers||3|
|ECE 8570||Neural Dynamics and Communication||3|
|or CMP_SC 8570||Neural Dynamics and Communication|
|ECE 8580||Machine Learning in Neuroscience||3|
|or CMP_SC 8580||Machine Learning in Neuroscience|
Core courses need to be taken before or parallel to the elective courses.