Introduction to Signal Detection and Estimation
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| Module name | Introduction to Signal Detection and Estimation |
|---|---|
| Type of module | Selectable mandatory module |
| Learning outcomes, competencies, qualification goals | The student is able to:
Learning results with regard to the objectives of the course of study:
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| Types of courses | 3 SWS (semester periods per week): 2 SWS lecture 1 SWS exercise |
| Course contents | Elements of hypothesis testing; mean-squared estimation covering the principle of orthogonality, normal equations, Wiener filters, related efficient numerical methods like Levinson-Durbin recursion, Kalman filters, adaptive filters; classification methods based on linear discriminants, kernel methods, support vector machines; maximum-likelihood parameter estimation, Cramer-Rao bound, EM algorithm |
| Teaching and learning methods (forms of teaching and learning) | Lecture, presentation, learning by teaching, self-regulated learning, problem-based learning |
| Frequency of the module offering | Summer term |
| Language | English |
| Recommended (substantive) requirements for the participation in the module | Knowledge on basic principles about random variables |
| Requirements for the participation in the module | Prerequisites according to examination regulations |
| Student workload | 180 h: 45h attendance studies 135 h personal studies |
| Academic performances | None |
| Precondition for the admission to the examination performance | None |
| Examination performance | Examination performance: oral examination (30 min.) |
| Number of credits of the module | 6 credits and 2 credits of them apply to the integrated key competencies |
| In charge of the module | Prof. Dr.Dahlhaus |
| Teacher of the module | Prof. Dr. Dahlhaus and co-workers |
| Forms of media | Projector, black board, piece of paper |
| Literature references |
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