Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 3 / Fall Semester | ||||
| Level of Course | 1st Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | DEPARTMENT OF COMPUTER ENGINEERING | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | EBUBEKİR KAYA (ebubekir@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | SEMA ATASEVER, NUH AZGINOĞLU, | ||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| Gain the ability to problem solving with artificial intelligence algorithms. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Can determine a problem is fit to AI methods or not. |
PO-4 Students gain the ability to apply knowledge of mathematics, science and engineering. PO-5 Students gain the ability to define, model, formulate and solve general engineering problems. PO-6 Students gain the ability to solve real life learning, inference, optimization, estimation, classification and recognition problems with artificial intelligence. PO-7 Students gain the ability to identify, define, formulate and solve problems specific to Computer Engineering. PO-16 Students gain the ability to work individually/in a group or with interdisciplinary teams. PO-19 Students develop self-renewal and researcher skills in order to adapt to innovations and developing technology. PO-20 Students gain the ability to design and conduct experiments, analyze and interpret the results |
Examination |
| LO-2 | Can choose an appropriate AI methods for a given problem. |
PO-4 Students gain the ability to apply knowledge of mathematics, science and engineering. PO-5 Students gain the ability to define, model, formulate and solve general engineering problems. PO-6 Students gain the ability to solve real life learning, inference, optimization, estimation, classification and recognition problems with artificial intelligence. PO-7 Students gain the ability to identify, define, formulate and solve problems specific to Computer Engineering. PO-16 Students gain the ability to work individually/in a group or with interdisciplinary teams. PO-19 Students develop self-renewal and researcher skills in order to adapt to innovations and developing technology. PO-20 Students gain the ability to design and conduct experiments, analyze and interpret the results |
Examination |
| LO-3 | Can implement an AI methods for a given problem. |
PO-4 Students gain the ability to apply knowledge of mathematics, science and engineering. PO-5 Students gain the ability to define, model, formulate and solve general engineering problems. PO-6 Students gain the ability to solve real life learning, inference, optimization, estimation, classification and recognition problems with artificial intelligence. PO-7 Students gain the ability to identify, define, formulate and solve problems specific to Computer Engineering. PO-16 Students gain the ability to work individually/in a group or with interdisciplinary teams. PO-19 Students develop self-renewal and researcher skills in order to adapt to innovations and developing technology. PO-20 Students gain the ability to design and conduct experiments, analyze and interpret the results |
Examination |
| LO-4 | Can know the searching algorithms, their advantages and disadvantages. |
PO-4 Students gain the ability to apply knowledge of mathematics, science and engineering. PO-5 Students gain the ability to define, model, formulate and solve general engineering problems. PO-6 Students gain the ability to solve real life learning, inference, optimization, estimation, classification and recognition problems with artificial intelligence. PO-7 Students gain the ability to identify, define, formulate and solve problems specific to Computer Engineering. PO-16 Students gain the ability to work individually/in a group or with interdisciplinary teams. PO-19 Students develop self-renewal and researcher skills in order to adapt to innovations and developing technology. PO-20 Students gain the ability to design and conduct experiments, analyze and interpret the results |
Examination |
| LO-5 | Can know the knowledge representation methods, their advantages and disadvantages. |
PO-4 Students gain the ability to apply knowledge of mathematics, science and engineering. PO-5 Students gain the ability to define, model, formulate and solve general engineering problems. PO-6 Students gain the ability to solve real life learning, inference, optimization, estimation, classification and recognition problems with artificial intelligence. PO-7 Students gain the ability to identify, define, formulate and solve problems specific to Computer Engineering. PO-16 Students gain the ability to work individually/in a group or with interdisciplinary teams. PO-19 Students develop self-renewal and researcher skills in order to adapt to innovations and developing technology. PO-20 Students gain the ability to design and conduct experiments, analyze and interpret the results |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| The history of AI, Blind Search Algorithms, Heuristic Search Algorithms, Local Search Algorithms, Genetic Algorithms, Game Algorithms, Prolog Programming Language, Knowledge Representation, Expert Systems, Machine Learning Algorithms, | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Course Introduction | Lecture, question-answer, discussion |
| 2 | The history of AI | Lecture, question-answer, discussion |
| 3 | Blind Search Algorithms | Lecture, question-answer, discussion |
| 4 | Heuristic Search Algorithms | Lecture, question-answer, discussion |
| 5 | Heuristic Search Algorithms | Lecture, question-answer, discussion |
| 6 | Local Search Algorithms | Lecture, question-answer, discussion |
| 7 | Genetic Algorithms | Lecture, question-answer, discussion |
| 8 | mid-term exam | |
| 9 | Game Algorithms | Lecture, question-answer, discussion |
| 10 | Prolog Programming Language | Lecture, question-answer, discussion |
| 11 | Knowledge Representation | Lecture, question-answer, discussion |
| 12 | Expert Systems | Lecture, question-answer, discussion |
| 13 | Machine Learning Algorithms | Lecture, question-answer, discussion |
| 14 | Machine Learning Algorithms | Lecture, question-answer, discussion |
| 15 | Machine Learning Algorithms | Lecture, question-answer, discussion |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc. | |
| Required Course instruments and materials | ||
| Auxiliary textbook, projection, computer | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 40 | |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | |||
| 9.Project | |||
| final exam | 16 | 60 | |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 2 | 14 | 28 |
| Outside Class | |||
| a) Reading | 2 | 14 | 28 |
| b) Search in internet/Library | 1 | 14 | 14 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 8 | 1 | 8 |
| mid-term exam | 2 | 1 | 2 |
| Own study for final exam | 8 | 1 | 8 |
| final exam | 2 | 1 | 2 |
| 0 | |||
| 0 | |||
| Total work load; | 90 | ||