Course Schedule and Catalog
Course Schedule - Spring Semester 2025
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
ELEC 575 001 (CRN: 20720)
LEARNING FROM SENSOR DATA
Long Title: LEARNING FROM SENSOR DATA
Department: Electrical & Computer Eng.
Instructor: Azhang, Behnam
Meeting: 2:30PM - 5:00PM T (13-JAN-2025 - 25-APR-2025)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 60
Section Enrolled: 0
Total Cross-list Max Enrollment: 60
Total Cross-list Enrolled: 0
Enrollment data as of: 14-NOV-2024 12:07PM
Final Exam: GR Course-Dept Schedules Exam
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. 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.