Course detail

Linear Algebra

FIT-ILGAcad. year: 2020/2021

Matrices and determinants. Systems of linear equations. Vector spaces and subspaces. Linear representation, coordinate transformation. Own values and own vectors. Quadratic forms and conics.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. RNDr. Dana Hliněná, Ph.D.

Department

Department of Mathematics (UMAT)

Learning outcomes of the course unit

The students will acquire an elementary knowledge of linear algebra and the ability to apply some of its basic methods in computer science.

Prerequisites

Secondary school mathematics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • Evaluation of the five written tests (max 40 points).

Exam prerequisites:
The minimal total score of 10 points gained out of the mid-term exams.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The students will get familiar with elementary knowledge of linear algebra, which is needed for informatics applications. Emphasis is placed on mastering the practical use of this knowledge to solve specific problems.

Specification of controlled education, way of implementation and compensation for absences

  • Participation in lectures in this course is not controlled.
  • The knowledge of students is tested at exercises; at five written tests for 8 points each, and at the final exam for 60 points.
  • If a student can substantiate serious reasons for an absence from an exercise, (s)he can either attend the exercise with a different group (please inform the teacher about that) or ask his/her teacher for an alternative assignment to compensate for the lost points from the exercise.
  • The passing boundary for ECTS assessment: 50 points.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Bečvář, J., Lineární algebra, matfyzpress, Praha, 2005. (in Czech).
Bican, L., Lineární algebra, SNTL, Praha, 1979. (in Czech).
Birkhoff, G., Mac Lane, S. Prehľad modernej algebry, Alfa, Bratislava, 1979. (in Slovak).
Havel, V., Holenda, J., Lineární algebra, STNL, Praha 1984. (in Czech).
Hejný, M., Zaťko, V, Kršňák, P., Geometria, SPN, Bratislava, 1985. (in Slovak).
Kolman B., Elementary Linear Algebra, Macmillan Publ. Comp., New York 1986.
Kolman B., Introductory Linear Algebra, Macmillan Publ. Comp., New York 1993.
Kovár, M.,  Maticový a tenzorový počet, FEKT VUT, Brno, 2013. (in Czech).
Neri, F., Linear algebra for computational sciences and engineering, Springer, 2016.
Olšák, P., Úvod do algebry, zejména lineární. FEL ČVUT, Praha, 2007. (in Czech).

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 1 year of study, winter semester, compulsory

  • Programme BIT Bachelor's 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. RNDr. Dana Hliněná, Ph.D.
Mgr. Irena Hlavičková, Ph.D.

Syllabus

  1. Systems of linear homogeneous and non-homogeneous equations. Gaussian elimination.
  2. Matrices and matrix operations. Rank of the matrix. Frobenius theorem.
  3. The determinant of a square matrix. Inverse and adjoint matrices. The methods of computing the determinant.The Cramer's Rule.
  4. Numerical solution of systems of linear equations, iterative methods.
  5. The vector space and its subspaces. The basis and the dimension. The coordinates of a vector relative to a given basis. The sum and intersection of vector spaces.
  6. The inner product. Orthonormal systems of vectors. Orthogonal projection and approximation. Gram-Schmidt orthogonalisation process.
  7. The transformation of the coordinates.
  8. Linear mappings of vector spaces. Matrices of linear transformations.
  9. Rotation, translation, symmetry and their matrices, homogeneous coordinates. 
  10. The eigenvalues and eigenvectors. The orthogonal projections onto eigenspaces.
  11. Conic sections.
  12. Quadratic forms and their classification using sections.
  13. Quadratic forms and their classification using eigenvectors.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Mgr. Gabriela Vážanová, Ph.D.
Mgr. Irena Hlavičková, Ph.D.
Mgr. Jiří Vítovec, Ph.D.
doc. RNDr. Dana Hliněná, Ph.D.
doc. RNDr. Zdeněk Svoboda, CSc.
Ing. Ondřej Lengál, Ph.D.

Syllabus

Examples of tutorials are chosen to suitably complement the lectures.

E-learning texts

Hliněná: Slajdy z prednášok
prednaska1.pdf 0.28 MB
prednaska2.pdf 0.18 MB
prednaska3.pdf 0.36 MB
prednaska4.pdf 0.28 MB

Elearning

eLearning: currently opened course