TTT4275 - Estimation, Detection and Classification


Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam 70/100 4 hours D
Portfolio 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. If the exam is to be repeated, the whole course needs to be taken.

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.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Third-year courses, level III


Term no.: 1
Teaching semester:  SPRING 2023

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Electronics and Telecommunications
  • Signal Processing
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Electronic Systems


Examination arrangement: Aggregate score

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 70/100 D
Room Building Number of candidates
Spring ORD Portfolio 30/100
Room Building Number of candidates
Summer UTS School exam 70/100 D
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"

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