inf401 - Foundations of Theoretical Computer Science (Course overview)
Department of Computing Science |
6 KP |
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Module components |
Semester courses Wintersemester 2022/2023 |
Examination |
Lecture
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Exercises
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2.01.401a - Übung Grundlagen der Theoretischen Informatik
- Cedric Richter
- Prof. Dr. Heike Wehrheim
- Jan Frederik Haltermann, M. Sc.
Monday: 10:15 - 11:00, weekly (from 17/10/22)
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2.01.401b - Übung Grundlagen der Theoretischen Informatik
- Cedric Richter
- Prof. Dr. Heike Wehrheim
- Jan Frederik Haltermann, M. Sc.
Monday: 11:00 - 11:45, weekly (from 17/10/22)
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2.01.401c - Übung Grundlagen der Theoretischen Informatik
- Prof. Dr. Heike Wehrheim
- Cedric Richter
- Jan Frederik Haltermann, M. Sc.
Wednesday: 10:15 - 11:00, weekly (from 19/10/22)
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2.01.401d - Übung Grundlagen der Theoretischen Informatik
- Prof. Dr. Heike Wehrheim
- Cedric Richter
- Jan Frederik Haltermann, M. Sc.
Wednesday: 11:00 - 11:45, weekly (from 19/10/22)
Die Vorlesung gibt eine Einführung in Grundlagen der theoretischen Informatik im Bereich formale Sprachen sowie Berechenbarkeit und Komplexität.
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2.01.401e - Übung Grundlagen der Theoretischen Informatik
- Prof. Dr. Heike Wehrheim
- Cedric Richter
- Jan Frederik Haltermann, M. Sc.
Friday: 12:15 - 13:00, weekly (from 21/10/22), Location: A01 0-005, V03 0-C003
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Hinweise zum Modul |
Prüfungszeiten |
At the end of the lecture period |
Module examination |
Written or oral exam |
Skills to be acquired in this module |
Introduction to the theory of automata, formal languages, computability, and complexity Professional competenceThe students: - Know different classes of languages (e.g. regular and context-free languages)
- Know automata models corresponding to the respective language classes (e.g. finite automata, pushdown automata, Turing machines)
- Construct automata, Turing machines, and grammars for given tasks
- Know equivalent formalisations of the concept of algorithm
- Classify functions as algorithmically computable and problems as algorithmically decidable
- Know and recognize undecidable problems
- Evaluate the complexity of algorithms
- Know problems that are solvable deterministically or nondeterministically in polynomial time
Methodological competenceThe students: - Learn about the power of abstract models of computation
Social competenceThe students: - Work together in small groups to solve problems
- Present solutions to problems to groups of other students
Self-competenceThe students: - Learn persistence in pursuing difficult tasks
- Learn precision in writing down solutions
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