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FEL3201 Data-Driven Modeling, Basic Course 8.0 credits

Information per course offering

Course offerings are missing for current or upcoming semesters.

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

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.

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

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

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.

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

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

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

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted

Postgraduate course

Postgraduate courses at EECS/Decision and Control Systems