ECE 5110
Last Updated
- Schedule of Classes - November 21, 2024 7:58PM EST
- Course Catalog - November 21, 2024 7:07PM EST
Classes
ECE 5110
Course Description
Course information provided by the Courses of Study 2024-2025.
Introduction to models for random signals in discrete and continuous time; Markov chains, Poisson process, queuing processes, power spectral densities, Gaussian random process. Response of linear systems to random signals. Elements of estimation and inference as they arise in communications and digital signal processing systems.
When Offered Fall.
Permission Note Enrollment limited to: graduate students.
Prerequisites/Corequisites Prerequisite: ECE 2720, ECE 3100, and ECE 3250 or equivalents.
Outcomes
- Knowledge of a variety of mathematical models for random phenomena.
- Ability to classify models with respect to stationarity, Markov property, asymptotics, and more.
- Ability to make optimal inferences and estimates with respect to such criteria as minimum error probability, and minimum mean square error.
- Become aware of applications to communications, machine learning, statistical physics and more.
- Response of linear systems to random process inputs (time permitting).
Regular Academic Session. Choose one lecture and one discussion. Combined with: ECE 4110
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Credits and Grading Basis
4 Credits Graded(Letter grades only)
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Class Number & Section Details
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Meeting Pattern
- MW Hollister Hall 320
- Aug 26 - Dec 9, 2024
Instructors
Zhao, Q
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Additional Information
Instruction Mode: In Person
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Class Number & Section Details
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Meeting Pattern
- F Hollister Hall 206
- Aug 26 - Dec 9, 2024
Instructors
Zhao, Q
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Additional Information
Instruction Mode: In Person
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