Signal spectra, linear time-invariant sytems, prediction and filtering, linear and non-linear models, identifiability, non-parametric methods, parameter estimation, maximum likelihood estimation, linear regression, least-squares estimation, the prediction error method, the instrumental variable method, subspace identification, kernel methods, support vector machines, convergence and consistency, modeling accuracy, Cramér-Rao lower bound, numerical optimization, recursive estimation, bias and variance errors, experiment design, applications oriented system identification, choice of identification criterion, model validation, model structure selection, system identification in practice.
FEL3201 Data-Driven Modeling, Basic Course 8.0 credits
Information per course offering
Information for Autumn 2025 Start 25 Aug 2025 programme students
- Course location
KTH Campus
- Duration
- 25 Aug 2025 - 24 Oct 2025
- Periods
Autumn 2025: P1 (8 hp)
- Pace of study
67%
- Application code
10629
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FEL3201 (Spring 2019–)Headings with content from the Course syllabus FEL3201 (Spring 2019–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After the course, the student should be able to:
· describe the general principles for system identification.
· identify systems in a satisfactory manner. This includes choice of excitation signals, model structure and estimation algorithm as well as proper use of model validation.
· analyse basic model properties, such as identifiability and accuracy (bias and variance errors).
Literature and preparations
Specific prerequisites
No information inserted
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
P, F
Examination
- EXA1 - Examination, 8.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
Other requirements for final grade
· 15 min oral presentation of a selected topic in one of the lectures
· 80% on weekly home-work problems
· project (preferably on a problem related to the student’s own research)
· 50 % on 72 h take home exam
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Education cycle
Third cycle