ELECTRICAL-ELECTRONICS ENGINEERING (MASTER) (WITH THESIS) (ENGLISH)
Qualification Awarded	Program Süresi	Toplam Kredi (AKTS)	Öğretim Şekli	Yeterliliğin Düzeyi ve Öğrenme Alanı
	2	120	FULL TIME	TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 7 QF-EHEA:Second Cycle TR-NQF-HE, ISCED (1997-2013): 52

Ders Genel Tanıtım Bilgileri

Course Code:

3026002015

Ders İsmi:

Switching and Automata Theory

Ders Yarıyılı:

Fall
Spring

Ders Kredileri:

Theoretical	Practical	Labs	Credit	ECTS
3	0	0	3	6

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Other Recommended Topics for the Course:

Type of course:

Department Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. Çağatay ULUIŞIK

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

The objectives of this course are not only to introduce students to the mathematical foundations of computation including automata theory and the theory of formal languages and grammars; but also to provide students with an understanding of such basic concepts as automata, the equivalent regular expressions, the equivalence of languages described by automata, regular expressions, pushdown automata, the equivalent context free grammars, the equivalence of languages described by pushdown automata, context free grammars, Turing machines and the equivalence of languages described by Turing machines.

Course Content:

Mathematical Tools (Definitions, Theorems, and Proofs); Types of Proofs; Regular Languages; Finite Automata; Nondeterminism; Regular Expressions; Nonregular Languages; Context-Free Languages; Context-free Grammars; Pushdown Automata; Non-context-free Languages; Turing Machines; Variants of Turing Machines; Definition of "Algorithm"; Decidability; Decidable Languages; Reducibility; NP-completeness; Reducibility; Recognizability

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Students will be able to analyze finite automata, deterministic and non-deterministic automata, regular expressions, pushdown automata, Turing machines, formal languages, and grammars
2) Students will be to demonstrate their the understanding of key notions, such as algorithm, computability, decidability, and complexity through problem solving.
3) Students will prove the basic results of the Theory of Computation.
2 - Skills
Cognitive - Practical
1) Students will be able to design finite automata, deterministic and non-deterministic automata, regular expressions, pushdown automata, Turing machines, formal languages, and grammars.
2) Students will be familiar with Turing Machines and Problem Classes.
3) Students will improve problem solving skills.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Course overview, formation of preliminary concepts, mathematical tools, definitions, theorems, and proofs, types of proofs	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
2)	Deterministic finite automata (DFA)	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
3)	Non-deterministic finite automata (NFA)	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
4)	Equivalence of DFA and NFA, and regular expressions	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
5)	Epsilon transition, pumping Lemma, pigeonhole principle, and closure properties	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
6)	Optimal DFA, and overview	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
7)	Context-free languages, context-free grammars, parse tree, ambiguity, closure properties	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
8)	Midterm exam
9)	Pushdown automata (PDA)	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
10)	Overview of context-free grammars, and Church-Turing hypothesis	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
11)	Turing Machines, Recognition and Computation, Church-Turing Hypothesis	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
12)	NP-completeness, decidability, reducibility, and recognizability	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
13)	NP-completeness, decidability, reducibility, and recognizability	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
14)	Practice or Review
15)	Final exam

Sources

Course Notes / Textbooks:	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.
References:	Michael Sipser, Introduction to the Theory of Computation, Cengage Learning, 3rd Edition, 2012. Daniel I. A. Cohen, Introduction to Computer Theory, Prentice-Hall, 2nd Edition, 1997. Z., Kohavi, N. K., Jha, "Switching and Finite Automata Theory", Cambridge University Press, 2009.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	3	6	2	4	5
Program Outcomes
1) Through scientific research in the field of Electrical-Electronics Engineering, they expand and deepen their knowledge, evaluate, interpret, and apply the information.
2) They have comprehensive knowledge of the current techniques and methods applied in Electrical-Electronics Engineering, as well as their limitations.
3) Using uncertain, limited, or incomplete data, they complement and apply knowledge through scientific methods; they can integrate information from different disciplines.
4) They are aware of new and emerging applications in Electrical-Electronics Engineering, and when necessary, they investigate and learn about them.
5) They define and formulate Electrical-Electronics Engineering problems, develop methods to solve them, and apply innovative approaches in the solutions.
6) They develop new and/or original ideas and methods; design complex systems or processes and develop innovative/alternative solutions in their designs.
7) They design and apply theoretical, experimental, and modeling-based research; they analyze and solve complex problems encountered during this process.
8) They can work effectively in both interdisciplinary and multidisciplinary teams, lead such teams, and develop solution approaches in complex situations; they can work independently and take responsibility.
9) They communicate effectively in both spoken and written forms using a foreign language at least at the B2 General Level of the European Language Portfolio.
10) They communicate the processes and results of their work in a systematic and clear manner, either in writing or verbally, in national and international contexts, both within and outside their field.
11) They are aware of the social, environmental, health, safety and legal aspects of Electrical and Electronics Engineering applications, project management and business life practices and are aware of the constraints these impose on engineering applications.
12) They observe social, scientific and ethical values in the stages of collecting, interpreting and announcing the data and in all professional activities.

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Through scientific research in the field of Electrical-Electronics Engineering, they expand and deepen their knowledge, evaluate, interpret, and apply the information.
2)	They have comprehensive knowledge of the current techniques and methods applied in Electrical-Electronics Engineering, as well as their limitations.
3)	Using uncertain, limited, or incomplete data, they complement and apply knowledge through scientific methods; they can integrate information from different disciplines.
4)	They are aware of new and emerging applications in Electrical-Electronics Engineering, and when necessary, they investigate and learn about them.
5)	They define and formulate Electrical-Electronics Engineering problems, develop methods to solve them, and apply innovative approaches in the solutions.
6)	They develop new and/or original ideas and methods; design complex systems or processes and develop innovative/alternative solutions in their designs.
7)	They design and apply theoretical, experimental, and modeling-based research; they analyze and solve complex problems encountered during this process.
8)	They can work effectively in both interdisciplinary and multidisciplinary teams, lead such teams, and develop solution approaches in complex situations; they can work independently and take responsibility.
9)	They communicate effectively in both spoken and written forms using a foreign language at least at the B2 General Level of the European Language Portfolio.
10)	They communicate the processes and results of their work in a systematic and clear manner, either in writing or verbally, in national and international contexts, both within and outside their field.
11)	They are aware of the social, environmental, health, safety and legal aspects of Electrical and Electronics Engineering applications, project management and business life practices and are aware of the constraints these impose on engineering applications.
12)	They observe social, scientific and ethical values in the stages of collecting, interpreting and announcing the data and in all professional activities.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Alan Çalışması
Bireysel çalışma ve ödevi

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 30
Midterms	1	% 30
Semester Final Exam	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	10	140
Midterms	1	2	2
Final	1	2	2
Total Workload			186