ELEC 483 - NEURAL SIGNAL PROCESSING
Long Title: MACHINE LEARNING AND SIGNAL PROCESSING FOR NEURO ENGINEERING
Department: Electrical & Computer Eng.
Grade Mode: Standard Letter
Language of Instruction: Taught in English
Course Type: Lecture
Credit Hours: 3
Must be enrolled in one of the following Level(s):
Prerequisite(s): (MATH 354 OR MATH 355 OR CAAM 335) AND (ELEC 303 OR STAT 305 OR STAT 310 OR ECON 307) AND (CAAM 210 OR COMP 140)
Description: This course covers advanced statistical signal processing and machine learning approaches for modern neuroscience data (primarily many-channel spike trains). Topics include latent variable models, point processes, Bayesian inference, dimensionality reduction, dynamical systems, and spectral analysis. Neuroscience applications include modeling neural firing rates, spike sorting, decoding. Graduate/Undergraduate Equivalency: ELEC 548. Recommended Prerequisite(s): ELEC 475 and STAT 413 and COMP 540 and (ELEC 242 or ELEC 243) Mutually Exclusive: Cannot register for ELEC 483 if student has credit for ELEC 548.