Artificial Intelligence Engineering (Master) (With Thesis) (English)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

General course introduction information

Course Code:

AIE507

Course Name:

Automata Theory

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	10

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

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. PINAR YILDIRIM

Course Lecturer(s):

Prof. Dr. SEMİH BİLGEN

Course Assistants:

Course Objective and Content

Course Objectives:

At the end of the course the student will be able to:
• Understand and elaborate on the mathematical definition of a Finite State Machine (FSM).
• Simplify a given regular expression (RE) and derive the FSM that accepts the language represented by it.
• Given an FSM, derive the RE that it accepts.
• Given a non-deterministic FSM, derive the deterministic FSM that accepts the same language.
• Given a RL, constructing the FSM that will accept it.
• Use the Pumping Lemma to show that a given language is NOT regular, and so, not acceptable by a FSM.

Course Content:

This course aims to introduce students to the fundamentals of formal languages and the theory of computation.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Ability to explain the concepts of data mining
2) Ability to apply data preprocessing techniques
3) Ability to use the algorithms of data mining.
4) Ability to use the tools of data mining
5) Ability to produce solutions in the subjects of data mining
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	• Introduction and course overview	Reading chapter 1
2)	• Formal languages
3)	Identify regular languages (RL), use regular espressions to represent regular languages, determine the regular language represented by a given regular expression
4)	Identify regular languages (RL), use regular espressions to represent regular languages, determine the regular language represented by a given regular expression
5)	• Deterministic FSM’s • Correspondence of FSM’s with regular languages
6)	• Deterministic FSM’s • Correspondence of FSM’s with regular languages
7)	• Identify and trace the behaviour of Non-Deterministic FSM’s (NFSM) • Determine the RL accepted by a given NFSM
8)	Midterm exam
9)	• Recognition and tracing the behaviour of Λ-NFSM’s • Determine the RL accepted by a given Λ-NFSM • Construction of equivalent non-Λ-NFSMs • Construction of equivalent DFSM’s.
10)	• Recognition and tracing the behaviour of Λ-NFSM’s • Determine the RL accepted by a given Λ-NFSM • Construction of equivalent non-Λ-NFSMs • Construction of equivalent DFSM’s.
11)	• Given a RL, constructing the Λ-NFSM to accept it • Convert Λ-NFSM to NFSM and to DFSM
12)	• Given a RL, constructing the Λ-NFSM to accept it • Convert Λ-NFSM to NFSM and to DFSM
13)	Non regular languages • Pumping Lemma • Using the PL to show that a language is NOT regular
14)	Non regular languages • Pumping Lemma • Using the PL to show that a language is NOT regular
15)	Final exam

Sources

Course Notes / Textbooks:	J. Martin, Introduction to Languages and the Theory of Computation, Mc Graw-Hill, Ed.3 (2003) or Ed. 4 (2010)
References:	İnternet Kaynakları

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
2) Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
3) The ability to identify, define, formulate and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
4) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
5) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
6) Ability to design experiments, conduct experiments, collect data, analyze and interpret results for the examination of engineering problems.
7) The ability to communicate effectively both verbally and in writing; at least one foreign language knowledge.
8) Knowledge of the necessity of lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Professional and ethical responsibility.
10) Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.
11) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.

Course - Learning Outcome Relationship

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

	Program Outcomes	Level of Contribution
1)	Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
2)	Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
3)	The ability to identify, define, formulate and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
4)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
5)	Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
6)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results for the examination of engineering problems.
7)	The ability to communicate effectively both verbally and in writing; at least one foreign language knowledge.
8)	Knowledge of the necessity of lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9)	Professional and ethical responsibility.
10)	Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.
11)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.

Learning Activity and Teaching Methods

Lesson
Project preparation

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Individual Project

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 10
Midterms	2	% 50
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Workload
Course Hours	16	48
Project	2	70
Midterms	12	130
Final	4	62
Total Workload		310