inf401 - Foundations of Theoretical Computer Science (Course overview)

inf401 - Foundations of Theoretical Computer Science (Course overview)

Department of Computing Science 6 KP
Module components Semester courses Examination
Lecture
Exercises
  • No access 2.01.401a - Show lecturers
    • Cedric Richter
    • Prof. Dr. Heike Wehrheim
    • Jan Frederik Haltermann, M. Sc.

    Monday: 10:15 - 11:00, weekly (from 17/10/22)

  • No access 2.01.401b - Show lecturers
    • Cedric Richter
    • Prof. Dr. Heike Wehrheim
    • Jan Frederik Haltermann, M. Sc.

    Monday: 11:00 - 11:45, weekly (from 17/10/22)

  • No access 2.01.401c - Show lecturers
    • Prof. Dr. Heike Wehrheim
    • Cedric Richter
    • Jan Frederik Haltermann, M. Sc.

    Wednesday: 10:15 - 11:00, weekly (from 19/10/22)

  • No access 2.01.401d - Show lecturers
    • Prof. Dr. Heike Wehrheim
    • Cedric Richter
    • Jan Frederik Haltermann, M. Sc.

    Wednesday: 11:00 - 11:45, weekly (from 19/10/22)

  • No access 2.01.401e - Show lecturers
    • 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

Hinweise zum Modul
Prerequisites
No participant requirement
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 competence
The 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 competence
The students:
  • learn about the power of abstract models of computation
Social competence
The students:
  • work together in small groups to solve problems
  • present solutions to problems to groups of other students
Self-competence
The students:
  • learn persistence in pursuing difficult tasks
  • learn precision in writing down solutions