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Course Catalog 2012-2013
SGN-2607 Statistical Signal Processing, 6 cr |
Additional information
Course website: http://www.cs.tut.fi/courses/SGN-2607/
Suitable for postgraduate studies
Person responsible
Heikki Huttunen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Final examination, weekly exercises and a Matlab assignment.
Completion parts must belong to the same implementation
Learning outcomes
After passing this course the student will understand what statistical parameter estimation means, and how the methods can be applied in signal processing. Additionally, the basics of statistical detection theory will be covered.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Basic concepts of estimation. What is an estimator and how to compare their performance. Cramer-Rao lower bound for estimator variance. | ||
| 2. | Classical tools for estimation of a deterministic parameter: minimum variance unbiased estimator (MVUE), sufficient statistic, best linear unbiased estimator (BLUE), least squares estimator. | ||
| 3. | Sparse and regularized estimators: Ridge regression and the LASSO. Design of sparse classifiers via regularized logistic regression. | ||
| 4. | Estimation of nondeterministic parameters: the Bayesian approach. | ||
| 5. | Basic principles of detection theory: Likelihood ratio test, ROC curve. |
Evaluation criteria for the course
The final grade comes from the final exam. The grade is incremented by one if at least 50% of weekly exercises are done.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Fundamentals of Statistical Signal Processing - Estimation Theory | Kay, S.M. | 0-13-042268-1 | English | |||
| Lecture slides | Statistical Signal Processing | Heikki Huttunen | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| MAT-33311 Statistics 1 | Advisable | 1 |
| MAT-33317 Statistics 1 | Advisable | 1 |
1 . The contents of the two courses are equivalent.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Lectures Excercises Practical works |
Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |