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Module code: PRI-INF2 |
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2V+2U (4 hours per week) |
5 |
Semester: 2 |
Mandatory course: yes |
Language of instruction:
German |
Assessment:
Written exam
[updated 19.02.2018]
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KIB-INF2 (P222-0017) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2021
, semester 2, mandatory course
KIB-INF2 (P222-0017) Computer Science and Communication Systems, Bachelor, ASPO 01.10.2022
, semester 2, mandatory course
PRI-INF2 (P222-0017) Production Informatics, Bachelor, SO 01.10.2023
, semester 2, mandatory course
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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.
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Recommended prerequisites (modules):
None.
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Recommended as prerequisite for:
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Module coordinator:
Prof. Dr. Damian Weber |
Lecturer: Prof. Dr. Damian Weber
[updated 07.08.2019]
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Learning outcomes:
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.
[updated 26.02.2018]
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Module content:
1. Graphs 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
[updated 19.02.2018]
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Teaching methods/Media:
[updated 19.02.2018]
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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
[updated 19.02.2018]
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Module offered in:
SS 2024
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