Course Schedule - Spring Semester 2018

     

ELEC 575 001 (CRN: 22347)

LEARNING FROM SENSOR DATA

Long Title: LEARNING FROM SENSOR DATA
Department: Electrical & Computer Eng.
Instructor: Aazhang, Behnaam
Meeting: 9:25AM - 10:40AM TR DCH 1070 (8-JAN-2018 - 20-APR-2018) 
Session: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
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: 99
Section Enrolled: 21
Total Cross-list Max Enrollment: 99
Total Cross-list Enrolled: 31
Enrollment data as of: 19-SEP-2020 4:58PM
 
Fees: None
 
Final Exam: GR Course-Dept Schedules Exam
 
Description: The first half of this course develops the basic machine learning tools for signals images, and other data acquired from sensors. Tools covered include principal components analysis, regression, support vector machines, neural networks, and deep learning. The second half of this course overviews a number of applications of sensor data science in neuroscience, image and video processing, and machine vision. Additional course work required beyond the undergraduate course requirements. Mutually Exclusive: Cannot register for ELEC 575 if student has credit for ELEC 475. Repeatable for Credit.