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Higher Mathematics 1

Module name (EN):
Name of module in study programme. It should be precise and clear.
Higher Mathematics 1
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Computer Science and Communication Systems, Master, ASPO 01.04.2016
Module code: KI735
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P222-0053
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.
2V (2 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.
3
Semester: 1
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam

[updated 29.06.2007]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

KI735 (P222-0053) Computer Science and Communication Systems, Master, ASPO 01.04.2016 , semester 1, 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).
30 class hours (= 22.5 clock hours) over a 15-week period.
The total student study time is 90 hours (equivalent to 3 ECTS credits).
There are therefore 67.5 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
KI835 Higher Mathematics 2
KI861 Data Mining
KI867


[updated 03.02.2011]
Module coordinator:
Prof. Dr. Barbara Grabowski
Lecturer:
Prof. Dr. Barbara Grabowski


[updated 01.04.2003]
Lab:
Applied Mathematics, Statistics, and eLearning (5306)
Learning outcomes:
Students will become acquainted with the fundamental terminology of graph theory and probability calculus and with the significance of these disciplines to information and communication technology.
 
After completing this course students will be able to independently solve simple problems concerning network optimization, coding and simulation.
 


[updated 29.06.2007]
Module content:
1. Introduction to graph theory
1.1. Graphs
1.2. Eulerian and Hamiltonian circuits
1.3. Minimum spanning trees
 
2. Fundamentals of probability theory
2.1. Fundamentals
2.2. Random variables and their distributions   
2.3. Special probability distributions
2.4. Limiting value theorems
2.5. Multidimensional stochastic variables
2.6. Stochastic processes
 
3. Mathematical methods used in simulating discrete information systems
3.1. Frequency and probability
3.2. Statistical methods


[updated 29.06.2007]
Recommended or required reading:
GRABOWSKI B., Stochastik für Kommunikationsinformatiker, e-learning book in the ActiveMath series.
BRANDSTÄDT A., Graphen und Algorithmen, B.G.Teubner Stuttgart, 1994


[updated 29.06.2007]
Module offered in:
WS 2016/17, WS 2015/16, WS 2014/15, WS 2013/14, WS 2012/13, ... 
[Fri Apr 26 15:46:53 CEST 2024, CKEY=hm1, BKEY=kim, CID=KI735, LANGUAGE=en, DATE=26.04.2024]