Course - Estimation, Detection and Classification - TTT4275
TTT4275 - Estimation, Detection and Classification
About
Examination arrangement
Examination arrangement: Aggregate score
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| School exam | 70/100 | 4 hours | D | |
| Project report | 30/100 |
Course content
Estimation, detection and classification are at the heart of most signal processing systems which are central in Information and Communication Technology (ICT) and also provide the foundation for data analytics in a broader context (e.g. finance, medicine, industry, earth science). This course gives an introduction to the basic techniques for estimation, detection and classification with focus on ICT applications and signal processing aspects. The generality of the tools is shown through a variety of selected problems, using real-world data from biomedical, multimedia, and speech applications. The course is divided into three modules: --- a) Estimation : Introduction, Minimum Variance Unbiased Estimators and Cramer-Rao Lower Bound, Linear Models and Estimators and Least Squares, Maximum Likelihood and Bayesian Estimation --- b) Detection : Introduction, Statistical decision theory, Binary hypothesis, Likelihood ratio test, Bayes risk, Neyman-Pearson, ROC/DET. Detection of respectively deterministic and random signals. --- c) Classification : Introduction, The theoretical optimal classifier, three basic classifier types, estimation and clustering in classifier design, evaluation of performance, short on state-of-the-art including machine learning.
Learning outcome
Knowledge : The candidate have (i) principal understanding of the concepts of estimation, detection and classification; (ii) detailed knowledge of the basic methods within the three topic as a gateway to more advanced techniques; (iii) practical understanding of how to model and solve a variety of real-world problems; and (iv) knowledge of the importance of data for design and evaluation. Skills : The candidate can (i) identify the needs for estimation, detection and classification in practical problems; (ii) select appropriate methods for a given problem; (iii) expand autonomously the knowledge with more advanced techniques if necessary; (iv) implement the method in Matlab/Python, and (v) evaluate the quality of a chosen method for a given problem.
Learning methods and activities
Lectures with focus on practical examples, exercises and a group based project.
Further on evaluation
The grade is based on a written exam 70% and project report 30%. The results for the parts are given in letter grades, and the entire portfolio is also assigned with a letter grade.
The project report is a technical report with description of the problem, of the techniques considered for the solution, with quantitative results and discussion about the results + attached code developed to generate the results.
The deadline for the report is communicated to the student on the first day of the course and made available in Blackboard. No feedback to the students, the results of the report are communicated to the students after the written exam.
The re-sit exam form may be changed from written to oral in August. If the exam is to be repeated, the whole course needs to be taken.
Recommended previous knowledge
Corresponding to TMA4245 Statistics and TTT4120 Digital Signal Processing.
Required previous knowledge
Knowledge of linear algebra, probability theory, random variables, and stochastic processes.
Course materials
The course material is announced at the first lecture.
No
Version: 1
Credits:
7.5 SP
Study level: Third-year courses, level III
Term no.: 1
Teaching semester: SPRING 2025
Language of instruction: English, Norwegian
Location: Trondheim
- Electronics and Telecommunications
- Signal Processing
Department with academic responsibility
Department of Electronic Systems
Examination
Examination arrangement: Aggregate score
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD School exam 70/100 D INSPERA
-
Room Building Number of candidates - Spring ORD Project report 30/100
-
Room Building Number of candidates - Summer UTS School exam 70/100 D INSPERA
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"