This lecture will prepare Engineering students with mathematical foundation with the following details: Solving LES with GA, Vector spaces, Matrix and linear maps, Linear system of equations, Gauss-algorithm and LU-decomposition, Eigenvalues and its properties, Properties of symmetric matrices, SVD, Generalised inverse problem, QR-decomposition, Norms, Numeric of eigenvalues, Errors, Best fit functions, ODE, Linear diff. eqn. with constant coeff., Linear diff. eqn. of higher order, Multivariate/Multivariable Calculus with more variables, Inverse and implicit functions, Taylor expansions, Symm. matrix and quadratic forms, Extreme values, Stability of ODE.
Syllabus and homeworks are provided later on the lecture's Moodle-site.
Tutorial will be given in groups. There will be 2 or 3 Groups depends on the number of participants, and this will be announced as well in Moodle.
Link zu den Modulbeschreibungen im Service
Pflichtmodul (AR-101) im Masterstudium Automation & Robotics.
Im Masterstudium Data Science ist dieses Modul NICHT wählbar; es muss ggf. als Zusatzfach absolviert werden, falls eine Zulassung mit Auflagen erfolgt ist (MD Req1).