ELEC 575: LEARNING FROM SENSOR DATA
Spring 2024 · Section 001 · CRN 20720
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. Cross-list: 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.
- Credits
- 3
- Instructors
- Behnam Azhang