Department of Computer Engineering

CE 315 | Course Introduction and Application Information

Course Name

Automata Theory and Formal Languages

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 315	FALL	3	2	4	7

Prerequisites	CE 215 To Succeed (To get a grade of at least DD)
Course Language	English
Course Type	Required (Core Course)
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	Discussion Problem Solving Q&A Critique Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Dr. Öğr. Üyesi Alper Demir
Course Lecturer(s)	Dr. Öğr. Üyesi Alper Demir
Assistant(s)	Araş. Gör. Melek Büşra Temuçin

Course Objectives

The aim of this course is to introduce automata theory and formal languages, which are one step more abstract than existing programming languages. Fundamental models of computation that form the basis for various branches of computer science, such as compiler design and software engineering, will be presented. At the end of the course, all students are expected to have mastered all these concepts from an engineering perspective.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Will be able to map the Chomsky language hierarchy to the corresponding automata and grammar types,	1.5					X
LO2	Will be able to trace a given automata or grammar,	2				X
LO3	Will be able to translate a given automaton or grammar into another equivalent form,	4			X
LO4	Design an automata or grammar for a given language,	3.2				X
LO5	Will be able to use LR(k) parsers for efficient processing of context-insensitive languages,	4		X
LO6	Will be able to define the concepts of polynomial time, non-deterministic polynomial time, NP-completeness, decidability and undecidability.	1.1		X

Course Description

In this course, the most fundamental theories of computer science will be covered, including regular expressions and context-free languages, finite and pushdown automata, Turing machines, computability, undecidability and problem complexity.

Related Sustainable Development Goals

Course Category	Core Courses	X
	Major Area Courses
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Deterministic Finite Automata	Chapter 1. Sections 1.1. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO1
2	Deterministic Finite Automata	Chapter 1. Sections 1.1. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO2
3	Nondeterministic finite automata	Chapter 1. Sections 1.2. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO4
4	Nondeterministic finite automata	Chapter 1. Sections 1.2. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO2
5	Regular Expressions	Chapter 1. Sections 1.3. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO3
6	Context-free Grammars	Chapter 2. Sections 2.1. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO1
7	Context-free Grammars	Chapter 2. Sections 2.1. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO4
8	Pushdown Automata	Chapter 2. Sections 2.2. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO2
9	Pushdown Automata	Chapter 2. Section 2.3.. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO3
10	Pushdown Automata	Chapter 2. Section, 2.4. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO5
11	Turing Machines	Chapter 3. Sections 3.1. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO1
12	Turing Machines	Chapter 3. Sections 3.2, 3.3. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO3
13	Complexity classes P, NP, and NP complete	Chapter 7. Sections 7.1-- 7.4. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO6
14	Decidability and undecidability	Chapter 4. Introduction to the theory of computation. Michael Sipser. ISBN 053494728X	LO6
15	Review of the Semester		-
16	Final Exam		-

Course Notes/Textbooks	Introduction to the theory of computation. 3rd Edition. Michael Sipser. ISBN 113318779X.
Suggested Readings/Materials	https://ocw.mit.edu/courses/18-404j-theory-of-computation-fall-2020/

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5	LO6
Quizzes / Studio Critiques	4	20	X	X	X	X	X	X
Homework / Assignments	1	10	X	X	X	X	X	X
Midterm	1	30	X	X	X	X
Final Exam	1	40	X	X	X	X	X	X
Total	7	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	16	2	32
Study Hours Out of Class	14	5	70
Field Work	-	-	-
Quizzes / Studio Critiques	4	3	12
Portfolio	-	-	-
Homework / Assignments	2	5	10
Presentation / Jury	-	-	-
Project	-	-	-
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	20	20
Final Exam	1	20	20
		Total	212

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics		LO6
	2	Science
	3	Basic Engineering
	4	Computation
	5	Related engineering discipline-specific topics					LO1
	6	The ability to apply this knowledge to solve complex engineering problems
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.					LO2
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions				LO4
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.			LO5	LO3
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote or hybrid)
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
	1	Knowledge of business practices such as project management and economic feasibility analysis
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.