Subject description
Vector spaces
-
Eigenvalues and eigenvectors
-
Generalized inverses
-
Systems of linear equations
Optional material:
-
Matrix factorizations and matrix norms
-
Partitioned matrices
-
Matrix derivatives
-
Quadratic forms
The subject is taught in programs
Objectives and competences
Student gets acquainted with linear algebra for use in statistics, especially in multivariat statistic methods.
Teaching and learning methods
Lectures, exercises, homeworks, projects, study of literature, consultations.
Expected study results
Knowledge and understanding:
-
vector spaces and matrices,
-
eigenvalues and eigenvectors,
-
inner product and adjacent matrices,
-
sebi-adjacent, pozitive definite, orthogonal in normal matrices,
-
generalized inverses,
-
solving systems of linear equations,
-
matrix factorizations,
-
matrix norms.
Basic sources and literature
J. R. Schott: Matrix analysis for statistics.
R. Horn, C. Johnson: Matrix analysis.
S. Lange: Linear algebra.
V. Omladič: Uporaba linearne algebre v statistiki.
Stay up to date
University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana