back to table of content

INFB  Mathematics II Course INF
Lecturers : Prof. Dr. Roland Uhl    eMail
Prof. Dr. Rolf Socher    eMail
Term 2
Course Classification : Informatik Bachelor CH 4
Language : Deutsch Type VÜ 
Type of examination : PL  Credits 5 
Method of evaluation : written examination 120 min 
Requirements : Mathematics I
Cross References :  
Previous knowledges : Mathematics I 
Aids and special features : Mode of assessment
Pass at course examination
Graded: yes
Continuous Evaluation for assignments.
Overall grade is the course examination grade. 
Teaching aims : Ability to do matrix calculations: equivalent transformations, application of Gauß algorithm;
confident complex arithmetic: arithmetic, trigonometric and Euler representation, calculation with complex numbers,
ability to compute determinants, invert matrices and to determine the rank of matrices, solving of linear systems, identification of eigenvalues and eigenvectors;
ability to understand and apply definitions of basic notions of vector arithmetic, ability to test n-dimensional vectors for linear independency and dependency
working knowledge of definitions, properties and application of the inner product, the vector product and mixed product
ability to describe lines and planes by equations and to compute meets, distances and angles
knowledge of how to compute translations, rotations and scalings, how to transform one coordinate system into another one.  
Contents :

Linear systems I: homogeneous and heterogeneous systems, matrices, equivalence of matrices, Gauß algorithm, theorems on solutions of linear systems;
algebraic structures with two operations: rings and fields, complex numbers, fundamental theorem of algebra
Linear systems II: determinants, Laplace rule, rank of a matrix, Cramer rule, inverse of square matrix, Determinantensatz, structure and geometrical representation of solution sets, direction angles and distances of lines and planes, meets of solution sets;
Vector spaces and linear maps: definition, linear independence, bases and subspaces, representation of vectors, linear maps, transformation of bases, Eigenvalues and Eigenvectors;
Euclidean spaces: inner product, dihedral angles, Gram Schmidt process for orthogonalisation 

Literature : Jänich K.: Lineare Algebra. 11. Aufl. Berlin: Springer Verlag 2008
Schubert M.: Mathematik für Informatiker. Wiesbaden: Vieweg und Teubner Verlag 2009
Socher R.: Mathematik für Informatiker. München: Hanser 2011
Teschl S. und Teschl G.: Mathematik für Informatiker, Band 1, Diskrete Mathematik und Lineare Algebra. 3. Aufl. Berlin, Heidelberg: Springer 2008 

back to table of content