Theory of Computation (ΠΛ2)

Graduate Courses Outlines - Department of Mathematics

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Graduate
Course Code	ΠΛ2
Semester	1
Course Title	Theory of Computation
Independent Teaching Activities	Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type	Specialization
Prerequisite Courses	Undergraduate courses in Automata Theory and Formal Languages, Data Structures and Algorithms
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English)
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes

The goal of this course is the deeper understanding of Automata Theory and Language, Computability Theory and Complexity Theory as well as the introduction of students to critical thinking and research process. During the course a detailed examination of the following topics are done: Finite Automata (Deterministic FA, Nondeterministic FA, FA with Epsilon-Transitions) and their applications, Regular Expressions and Languages, Properties of Regular Languages Context-Free Grammars and languages, Pushdown Automata (Deterministic PDA, Acceptance by Fina lState, Acceptance by Empty Stack) , Properties of Context-Free Languages Turing Machines (Standard TM, Multitrack TM, Two-Way Tape TM, Multitape TM, Nondeterministic TM) Decidability and Computability Computational Complexity After completing the course the student can handle: theoretical documentation of problems solving exercises tracking applications which related to Finite Automata, Pushdown Automata, and Turing Machines as well as to Decidability and Computability and to Computational Complexity.

General Competences

Independent work Bibliographic search Effective selection and Design of the required machine and language.

Syllabus

• Properties of the Computation Theory Mathematical Models
• Problems classification to solvable and unsolvable
• Solvable Problems Classification

Teaching and Learning Methods - Evaluation

Delivery	Face to face
Use of Information and Communications Technology	Yes
Teaching Methods	Activity Semester Workload Lectures 39 Self study 78 Exercises 70.5 Course total 187.5
Student Performance Evaluation	Final essays (40%). Exercises - questions requiring critical thinking (30%). Presentations of related issues (30%).