| ARTIFICIAL INTELLIGENCE ENGINEERING | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP3008 | Formal Languages and Automata Theory | Spring | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Must Course |
| Course Level: | Bachelor |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi TEVFİK AYTEKİN |
| Course Objectives: | The course introduces basic formal languages and abstract computational models. The power and limitations of these languages and models will studied. Undecidable and NP-complete problems will be introduced. |
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The students who have succeeded in this course; I. Be able to identify classes of formal languages, computational models and their relationships. II. Be able design regular expressions and finite state automata. III. Be able to convert Nondeterministic Finite Automata to Deterministic Finite Automata. IV. Be able to convert regular expressions to Nondeterministic Finite Automata. V. Be able to design grammars and push down automata VI. Be able to design Turing machines VII. Be able prove theorems in automata theory. VIII. Become familiar with undecidable and NP-complete problems and apply/report heuristic algorithms for solving NP-complete problems in a group project. |
| Introduction, strings and languages, regular languages, finite automata, designing finite automata, nondeterminism, equivalence of NFAs and DFAs, regular Expressions, equivalence with finite automata, pumping lemma for regular languages, context-free Grammars, designing CFGs, Chomsky normal form, pushdown automata, equivalence with context-free grammars, non-context-free languages, Turing machines, examples of Turing machines, design of Turing machines, halting problem, undecidable problems, NP-complete problems. |
| Week | Subject | Related Preparation | |
| 1) | Introduction, Strings, and Languages. | ||
| 2) | Finite Automata | ||
| 3) | Nondeterminism | ||
| 4) | Regular Expressions | ||
| 5) | Nonregular Languages | ||
| 6) | Context-Free Grammars | ||
| 7) | Pushdown Automata | ||
| 8) | Midterm Exam | ||
| 9) | Deterministic Context-Free Languages | ||
| 10) | Turing Machines. | ||
| 11) | Variants of Turing Machines | ||
| 12) | Undecidability | ||
| 13) | The Class P and The Class NP | ||
| 14) | NP-Completeness and Additional NP-Complete Problems | ||
| 14) | NP-Completeness and Additional NP-Complete Problems | ||
| Course Notes: | Sipser, M. Introduction to the Theory of Computation, (3rd edition), 2012. |
| References: | Yok - None |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | 1 | % 20 |
| Seminar | % 0 | |
| Midterms | 1 | % 40 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentations / Seminar | |||
| Project | 5 | 30 | |
| Homework Assignments | 11 | 33 | |
| Quizzes | 4 | 8 | |
| Preliminary Jury | |||
| Midterms | 5 | 25 | |
| Paper Submission | |||
| Jury | |||
| Final | 5 | 25 | |
| Total Workload | 163 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Have sufficient background in mathematics, science and artificial intelligence engineering. | 5 |
| 2) | Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions. | 5 |
| 3) | Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose. | 5 |
| 4) | Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction. | 5 |
| 5) | Select and use modern techniques and tools necessary for engineering applications. | 5 |
| 6) | Design and conduct experiments, collect data, and analyse and interpret results. | 5 |
| 7) | Work effectively both as an individual and as a multi-disciplinary team member. | |
| 8) | Access information via conducting literature research, using databases and other resources | |
| 9) | Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning. | |
| 10) | Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field. | |
| 11) | Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level. | |
| 12) | Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age. | |
| 13) | Have a sense of professional and ethical responsibility. | |
| 14) | Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices. |