htw saar Piktogramm QR-encoded URL
Back to Main Page Choose Module Version:
emphasize objectives XML-Code

flag

Higher and Applied Mathematics

Module name (EN):
Name of module in study programme. It should be precise and clear.
Higher and Applied Mathematics
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Engineering and Management, Master, ASPO 01.10.2004
Module code: MAM-7.1
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
8V (8 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
10
Semester: 7
Mandatory course: yes
Language of instruction:
German
Assessment:
Two written exams

[updated 12.09.2004]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

MAM-7.1 Engineering and Management, Master, ASPO 01.10.2004 , semester 7, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
120 class hours (= 90 clock hours) over a 15-week period.
The total student study time is 300 hours (equivalent to 10 ECTS credits).
There are therefore 210 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr.-Ing. Helge Frick
Lecturer: Prof. Dr.-Ing. Helge Frick

[updated 06.09.2004]
Learning outcomes:
The focus of this course is on teaching how to apply numerical methods and simulation techniques to engineering problems. Areas covered within the field of statistical analysis of complex data sets include: techniques of experimental planning, statistical quality control and multivariate statistical data analysis.

[updated 12.09.2004]
Module content:
Numerical Mathematics and Simulation II
 1. Introduction to MATLAB, SIMULINK and FEMLAB
 2. Discrete and fast Fourier transforms
 3. Generating curves and surfaces (spline and curve fitting toolboxes)
 4. Partial differential equations (boundary value and initial boundary value problems)
 5. Numerical solutions of PDEs (FEM, BEM, FVM, FDM)
 
Statistics and Analysis
I. Analysis
   1. Functions with several independent variables
   2. Vector analysis
II. Statistics
   1. Descriptive statistics
   2. Probability calculus
   3. Introduction to the methods of inferential statistics
   4. Introduction to the statistics package R
   5. Advanced statistical methods

[updated 14.08.2012]
Teaching methods/Media:
Lecture notes:  ‘Deskriptive Statistik’, and useful formulae (set 1)
Lecture notes:  ‘Wahrscheinlichkeitsrechnung’, and useful formulae (set 2)

[updated 12.09.2004]
Recommended or required reading:
BURG:  Höhere Mathematik für Ingenieure, Band 3+4+5, Teubner Verlag
KNABNER/ANGERMANN:  Numerik partieller Differentialgleichungen, Springer
WEBER:  Statistik für Ingenieure, Teubner Vlg. Stuttgart
HARTUNG, ERPELT:  Multivariate Statistik, Oldenbourg-Verlag
WALZ, GRABOWSKI:  Lexikon der Stochastik mit Beispielen, Spektrum Akademischer Verlag

[updated 12.09.2004]
 
[Wed Apr 24 06:20:11 CEST 2024, CKEY=mhuam, BKEY=mm0, CID=MAM-7.1, LANGUAGE=en, DATE=24.04.2024]