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Syllabus for

Academic year
SSY205 - Matrix analysis with applications, advanced level  
Matrisanalys med tillämpningar, avancerad nivå
Syllabus adopted 2021-04-29 by Head of Programme (or corresponding)
Owner: MPCOM
7,5 Credits
Grading: UG - Pass, Fail
Education cycle: Second-cycle
Main field of study: Electrical Engineering

The course round is cancelled. For further questions, please contact the director of studies MPCOM: COMMUNICATION ENGINEERING, MSC PROGR, contact information can be found here. This course round is planned to be given every other year.

Teaching language: English
Application code: 13118
Open for exchange students: Yes

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0108 Project 7,5 c Grading: UG   7,5 c    

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Tomas McKelvey

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General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Mathematical analysis and linear algebra.


Matrix analysis plays a key role in many engineering diciplines both concerning design tools and algorithm as well as for performance analysis. This course will provide a working knowledge of linear algebra and matrix analysis from a user's point of view. Applications in subspace-based methods for signal processing, multivariate statistics and linear systems are considered.

Learning outcomes (after completion of the course the student should be able to)

- solve linear equations
- describe rank, nullspace and range space of a matrix
- describe, solve and analyse multivariable least-squares problems
- use Kronecker products to reformulate and analyse matrix equations
- describe eigenvalues, similarity transforms and quadratic forms
- describe singular value decomposition and use it for analysis
- use matrix culculus to perform analysis


Matrices and Gaussian elimination, Vector spaces and linear equations,
Orthogonality and projections, determinants, diagonalization, eigenvalues and eigenvectors, singular value decomposition. subspace-based methods, quadratic-forms, Kronecker products, matrix calculus, Lyapunov equations and sample covariance statistics. Examples from signal processing and estimation.


Lectures, Tutorials and Home assignments


See course homepage

Examination including compulsory elements

Written examination

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

Page manager Published: Mon 28 Nov 2016.