Course syllabus adopted 2019-02-07 by Head of Programme (or corresponding).
Overview
- Swedish nameMatrisanalys med tillämpningar, avancerad nivå
- CodeSSY205
- Credits7.5 Credits
- OwnerMPCOM
- Education cycleSecond-cycle
- Main field of studyElectrical Engineering
- DepartmentELECTRICAL ENGINEERING
- GradingUG - Pass, Fail
Course round 1
- Teaching language English
- Application code 13118
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0108 Project 7.5 c Grading: UG | 7.5 c |
In programmes
Examiner
Tomas McKelvey- Deputy Head Of Department, Electrical Engineering
Eligibility
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.Aim
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
Content
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.