Application deadline

Type of course

Admission requirements

A relevant undergraduate bachelor Engineering programme with minimum 30 credits mathematics/statistics topics

Application code: 9371

Obligatory prerequisites

Course content

Recommended prerequisites

Objectives of the course

After completing the linear algebra course the candidate:

Knowledge: K1: Has advanced knowledge of concepts within linear algebra. This includes particular and general vector spaces, inner product spaces, basis and subspaces, linear transformations with matrix representation, the Gram-Schmidt process and least-squares problems. Further, has knowledge of extension of the theory of eigenvalues and eigenvectors, diagonalization with generalizations and singular value decompositions. K2: Has thorough knowledge of central theories and methodologies within the listed concepts in linear algebra and know how to apply these in mathematical problems. K4: Can analyse formulated linear algebra problems and identify methods to solve these.

Skills: After completing the linear algebra course the candidate: S3: Can recognize and identify linear problems and formulate them in terms of linear systems. S2: Can analyse and deal critically with theories in linear algebra and use these to structure and formulate scholarly arguments. S3: Can utilise existing interpretations and relevant methods within the field to accomplish a task. S3: Can use relevant methods within the field.

General competence: After completing the linear algebra course the candidate: GC1: Can analyse relevant linear problems. GC2: Can apply the knowledge and skills within linear algebra to carry out assignments. GC3: Can communicate about different aspects in linear algebra, particularly explaining in mathematical terms how to deal with mathematical tasks. GC4: Can use the knowledge for concepts, theories and methods in linear algebra in other engineering areas.

Teaching and Learning Methods: The linear algebra course is structured with a concentrated timetable in which students work only with this subject for two non-consecutive week, having two other courses and a reading in between. The course is R&D-based, utilizing a number of different teaching strategies to improve the learning of linear algebra. This includes traditional lectures, video recording of lectures, flipped classroom arrangements, small video supplies with teacher designed videos in which different approaches to the same linear algebra concepts are presented, task solving sessions and self-studies. Exercises are carefully selected to underpin the theory.

Workload: Two distributed assignments are to be completed. An obligatory questionnaire must be completed, in which students are encouraged to reflect on their learning goals in the course. Form of assessment: A final summative exam. Selected solutions from the exam will be graded externally.

Language of instruction and examination

Teaching methods

Lectures and task solution sessions. Video lectures.

Two distributed assignments to be completed.

Final, summative exam.

Assessment

Mandatory tasks: One mandatory questionnaire is to be submitted before the exam.

Assessement: 4 hour written exam accounting for the total grade in the course. Grading will be done by using A-F grading scale, F is Fail. A re-sit exam will be arranged for this course.

Recommended reading/syllabus

David Lay: Linear Algebra and Its Applications, latest edition.

Compendia: 'Linear systems, matrices and determinants', 'Real eigenvalues and eigenvectors'

Additional theory, texts and examples on It's learning. 

Lecture notes.  Web lectures.

Additional literature

G. Strang: Linear algebra and its applications

H. Anton: Elementary linear algebra

E. Kreyszig: Advanced engineering mathematics.