Machine Learning and Signal Processing for Neuro Engineering

CT Score

1500

#0 of 0 in ELEC

Difficulty

—/10

Workload

—h/wk

Median grade

—

from grade distribution

Would take again

—

n = 0 verified

Grade distribution

No distribution published for this course.

By professor: same course, different game

PROFESSORSCOREGRADEHRS/WKTERMS

Professor ratings →

Sections

No section data for this term yet.

In the tree

CMOR 220 or COMP 140 Description: This course covers signal processing and machine learning approaches for modern neuroscience data. Topics include latent variable models, point processes, dimensionality reduction, dynamical systems, and spectral analysis. Neuroscience applications include modeling neural firing rates, spike sorting, encoding, decoding, and electrical stimulation. Students will be introduced to public toolboxes that implement these methods. Cross-list: ELEC 548 , BIOE 548 . Graduate/Undergraduate Equivalency: ELEC 548 . Recommended Prerequisite(s): ( MATH 354 OR MATH 355 OR CMOR 302 OR CMOR 303 ) AND ( ELEC 303 OR STAT 305 OR STAT 310 OR ECON 307 ) Mutually Exclusive: Cannot register for ELEC 483 if student has credit for ELEC 548→ELEC 483→unlocks 0 courses

No mapped courses require this one yet. See on the map →

Study groups

0 active

🔒 Verified-only writes. You post as “sophomore, lecture section”: real enough to trust, anonymous enough to be honest.