Course
AM - Applied Mathematics
PDF Course Catalog Deutsche Version: AM
Version: 2 | Last Change: 12.02.2021 13:24 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben
| Long name | Applied Mathematics |
|---|---|
| Approving CModule | AM_BaET |
| Responsible |
Prof. Dr. Beate Rhein
Professor Fakultät IME |
| Level | Bachelor |
| Semester in the year | winter semester |
| Duration | Semester |
| Hours in self-study | 96 |
| ECTS | 5 |
| Professors |
Prof. Dr. Beate Rhein
Professor Fakultät IME |
| Requirements | Apply basic mathematical knowledge, especially functions and differential calculus Be able to apply methods of linear algebra Apply basic concepts of programming |
| Language | German |
| Separate final exam | Yes |
Literature
Knorrenschild: Numerische Mathematik (Fachbuchverlag)
Papula: Mathematik für Ingenieure und Naturwissenschaftler, Band 1+2 (Vieweg)
Papula: Mathematik für Ingenieure und Naturwissenschaftler, Band 1+2 (Vieweg)
Final exam
Details
The theoretical basics and the associated calculation methods are tested in a written exam. The programming skills required to implement an algorithm are tested in a programming test.Minimum standard
Both parts of the examination must be passed with at least 4.0. The written exam and the programming test are included proportionally in the module grade. The weighting is currently 70% for the written exam and 30% for the programming test.Exam Type
The theoretical basics and the associated calculation methods are tested in a written exam. The programming skills required to implement an algorithm are tested in a programming test.Learning goals
Knowledge
Computer arithmetic
Error calculation, condition of a matrix
Gaussian algorithm with column pivoting
Interpolation
Root finding algorithms (bisection, Newton, variants of Newton, fixed point iteration)
Iteration methods for systems of linear equations
Regression analysis
Probability theory
Error calculation, condition of a matrix
Gaussian algorithm with column pivoting
Interpolation
Root finding algorithms (bisection, Newton, variants of Newton, fixed point iteration)
Iteration methods for systems of linear equations
Regression analysis
Probability theory
Skills
be able to estimate the propagation of measurement errors
be able to apply and code numerical algorithms
be able to set up trend functions
be able to handle probabilities
be able to apply and code numerical algorithms
be able to set up trend functions
be able to handle probabilities
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Lecture | 2 |
| Exercises (whole course) | 1 |
| Exercises (shared course) | 0 |
| Tutorial (voluntary) | 0 |
Special literature
keine/none
Special requirements
none
Accompanying material
Lecture slides online
Exercises online
Teaching videos online
Instructions for programming online
Exercises online
Teaching videos online
Instructions for programming online
Separate exam
none
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