Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 2 / Spring Semester | ||||
| Level of Course | Short Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | COMPUTER PROGRAMMING | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | ORÇUN BAĞRA (orcunbagra@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| It is aimed to provide students to learn the basic concepts of artificial intelligence in search, play, logic, learning. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Learn the concepts of intelligence and artificial intelligence |
PO-51 Have knowledge about artificial intelligence. |
Examination Term Paper |
| LO-2 | Students have knowledge about artificial intelligence applications |
PO-51 Have knowledge about artificial intelligence. |
Examination Term Paper |
| LO-3 | Learning to search, play games, agents |
PO-51 Have knowledge about artificial intelligence. |
Examination Term Paper |
| LO-4 | Learn about machine learning, classification and clustering |
PO-51 Have knowledge about artificial intelligence. |
Examination Term Paper |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Concepts of intelligence and artificial intelligence, search algorithms, intelligent agents, machine learning, classification, clustering, artificial neural networks, genetic algorithms, fuzzy logic | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to artificial intelligence | Narration, Question-Answer. |
| 2 | Intelligent agents | Narration, Question-Answer. |
| 3 | Search algorithms | Narration, Question-Answer. |
| 4 | Machine Learning – Normalization | Narration, Question-Answer. |
| 5 | Classification, confusion matrix | Narration, Question-Answer. |
| 6 | Clustering, contingency matrix | Narration, Question-Answer. |
| 7 | Genetic Algorithms | Narration, Question-Answer. |
| 8 | mid-term exam | |
| 9 | Genetic Algorithms | Narration, Question-Answer. |
| 10 | Artificial Neural Networks | Narration, Question-Answer. |
| 11 | Artificial Neural Networks | Narration, Question-Answer. |
| 12 | Fuzzy Logic | Narration, Question-Answer. |
| 13 | Students Presentations | Narration, Question-Answer. |
| 14 | Students Presentations | Narration, Question-Answer. |
| 15 | Students Presentations | Narration, Question-Answer. |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Russell, S.J. And Norvig, P., “Artificial Intelligence : A Modern Approach”, Third Edition, Prentice-Hall, 2009. (AIMA) | |
| 2 | Sunumlar | |
| 3 | Yapay Zeka Uygulamaları, Seçkin Yayıncılık, 2016 | |
| Required Course instruments and materials | ||
| Course book, laptop computer, projector | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 20 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | 15 | 1 | 20 |
| 9.Project | |||
| final exam | 16 | 1 | 60 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 4 | 14 | 56 |
| Outside Class | |||
| a) Reading | 3 | 8 | 24 |
| b) Search in internet/Library | 3 | 7 | 21 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 2 | 4 | 8 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 2 | 2 | 4 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 2 | 2 | 4 |
| final exam | 2 | 1 | 2 |
| 0 | |||
| 0 | |||
| Total work load; | 120 | ||