ELEC 575 - LEARNING FROM SENSOR DATA
Long Title: LEARNING FROM SENSOR DATA
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):
Description: Basic information theoretic metrics and probabilistic machine learning tools for signals, images, and other data acquired from sensors, including graphical models, density estimation, principal components analysis, support vector machines, and source separation. Additional course work required beyond the undergraduate course requirements. Graduate/Undergraduate Equivalency: ELEC 475. Mutually Exclusive: Cannot register for ELEC 575 if student has credit for ELEC 475. Graduate/Undergraduate Equivalency: ELEC 475. Recommended Prerequisite(s): Introductory background in probability theory and statistics. Mutually Exclusive: Cannot register for ELEC 575 if student has credit for ELEC 475.