Class Restriction And Registration Summary


EE 60563 - Section 01: Random Vectors, Det. & Est. (CRN 11560)

Course Description:
Fundamentals of probability, random variables, and detection and estimation theory for signal processing, communications, and control. Vector spaces of random variables. Bayesian and Neyman-Pearson hypothesis testing. Bayesian and maximum likelihood estimation. Minimum-variance unbiased estimators and the Cramer-Rao bounds. (Fall)

Associated Term: Fall Semester 2012
Campus: Main
Credits: 3
Grade Mode: Standard Letter
Course may not be repeated

Must be enrolled in one of the following Levels:
Employee Non-Degree (EM) ,  Graduate Non-Degree (GD) ,  Graduate (GR)
Must be enrolled in one of the following Campuses:
Main (M)

Course Attributes:
ZTST - Final exam

Registration Availability (Overflow: Off )
  Maximum Actual Remaining
TOTAL 25 10 15

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Instructor's Description of Course » Hochwald, Bertrand » EE  60563 - Section 01:  Random Vectors, Det. & Est. (CRN 11560)
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Enrollment History » EE 60563 Random Vectors, Det. & Est. (CRN 11560)
Enrollment over the last three years
Course was recently taught in FA09, FA11
Average number of students: 15
Composition of Students First Year Soph Junior Senior/5th Grad/Prof
Arts and Letters          
Engineering         100%

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