|Module name (EN): Informatics 2|
|Degree programme: Computer Science and Communication Systems, Bachelor, ASPO 01.10.2017|
|Module code: KIB-INF2|
|Hours per semester week / Teaching method: 2V+2U (4 hours per week)|
|ECTS credits: 5|
|Mandatory course: yes|
|Language of instruction:
|Applicability / Curricular relevance:
KIB-INF2 (P222-0017) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2017, semester 2, mandatory course
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
|Recommended prerequisites (modules):
KIB-INF1 Informatics 1
KIB-MAT1 Mathematics 1
|Recommended as prerequisite for:
KIB-RN Computer Networks
KIB-SDSA Simulation of Discrete Systems with AnyLogic
Prof. Dr. Damian Weber
Dipl.-Inform. Marion Bohr (exercise)
Thorsten Jakobs, M.Sc. (exercise)
After successfully completing this course, students will understand the formulation of different algorithmic problems as a graph problem.
Students will be able to solve graph problems algorithmically. The knowledge about data structures and basic algorithmic techniques acquired in the course "Informatics 1" will be applied to solve these problems. In this way, students will acquire the skills required to analyze more complex algorithms.
Finally, an intuitive introduction to important complexity classes will provide the basis for understanding the algorithmic solvability of problems. The approaches of Greedy algorithms and dynamic programming will be understood as techniques for solving difficult algorithmic problems approximately and efficiently. By analyzing the consumption of resources, students will be able to decide for individual problems whether efficient, exact or heuristic procedures are available for solving them.
1.1 Data structures
1.2 Basic algorithms
1.3 Shortest paths
1.4 Connected components
2. Problem solving techniques
2.1 Dynamic programming
2.2 Greedy algorithms
2.3 Analytical techniques of approximate methods
|Recommended or required reading:
Cormen Th., Leiserson Ch., Rivest R., Introduction to Algorithms, Oldenbourg, 2013
Sedgewick R., Wayne K., Algorithmen und Datenstrukturen, Pearson Studium, 2014
|Module offered in: |
SS 2022, SS 2021, SS 2020, SS 2019, SS 2018
[Fri Aug 19 16:19:39 CEST 2022, CKEY=ki2, BKEY=ki2, CID=KIB-INF2, LANGUAGE=en, DATE=19.08.2022]