VBM685 - STATISTICAL SIGNAL PROCESSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| STATISTICAL SIGNAL PROCESSING | VBM685 | Any Semester/Year | 3 | 0 | 3 | 6 |
| Prequisites | - | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving | |||||
| Instructor (s) | Prof. Dr. A. Salim Kayhan | |||||
| Course objective | Successful students are expected to gain the following abilities: Knowledge of basic estimation, filtering, prediction methods such as Bayes, MAP, MLE, LMSE, Wiener, Levinson ve Kalman filters. | |||||
| Learning outcomes |
| |||||
| Course Content | Metric space, inner product, norm etc. definitions. Review of Probability and stochastic processes. Estimation methods: Bayes, MAP, MLE, LMSE. Filtering, estimation and prediction methods: Wiener, Levinson ve Kalman filters. | |||||
| References | T. Moon and W. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice-Hall. S.J. Orfanidis, Optimum Signal Processing, McGraww Hill. S. Kay, Fundamentals of Statistical Signal Processing, Vol.I-II, Prentice Hall. Lecture Notes. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Metric Spaces. |
| Week 2 | Norms, Orthogonal Spaces, Projections, Random Vectors. |
| Week 3 | Orthogonal Projections, Gram-Schmidt Orthogonalization. |
| Week 4 | Random Processes, Gaussian Processes, Markov Processes. |
| Week 5 | Random State Models. |
| Week 6 | Analysis of Systems, Spectral Factorization, Rational Modeling. |
| Week 7 | Bayesian Estimation, MAP, MLE,MSE. |
| Week 8 | LMSE. |
| Week 9 | Term Exam. |
| Week 10 | Wiener Filter. |
| Week 11 | Wiener Filter. |
| Week 12 | Levinson Filter. |
| Week 13 | Kalman Filter |
| Week 14 | Kalman Filter |
| Week 15 | Final exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 8 | 15 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 35 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 9 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 4 | 56 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 8 | 5 | 40 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 25 | 25 |
| Total Workload | 38 | 62 | 188 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has detailed knowledge about data and knowledge engineering (DKE). | X | ||||
| 2. Has a good understanding of common concepts such as abstraction, complexity, security, concurrency, software lifecycle and applies their expertise to the effective design, development and management of IS. | X | ||||
| 3. Understands the interaction of theory and practice and the links between them. | X | ||||
| 4. Has the ability to think at different levels of abstraction and detail; understands that an IS can be considered in different contexts, going beyond narrowly identifying implementation issues. | X | ||||
| 5. Solves any technical or scientific problem independently and presents the best possible solution; has the communication skills to clearly explain the completeness and assumptions of their solution. | X | ||||
| 6. Completes a project on a larger scale than an ordinary course project in order to acquire the skills necessary to work efficiently in a team. | X | ||||
| 7. Recognises that the field of DKE is rapidly evolving. Follows the latest developments, learns and develops skills throughout their career. | X | ||||
| 8. Recognises the social, legal, ethical and cultural issues related to DKE practice and conduct professional activities in accordance with these issues. | X | ||||
| 9. Can make oral presentations in English and Turkish to different audiences face-to-face, in writing or electronically. | X | ||||
| 10. Recognises that DKE has a wide range of applications and opportunities. | X | ||||
| 11. Is aware that DKE interacts with different fields, can communicate with experts from different fields and can learn necessary field knowledge from them. | X | ||||
| 12. Define a research problem and use scientific methods to solve it. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest