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 | KADİR HALTAŞ (haltaskadir@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim of this course is to introduce students to the fundamental concepts, techniques, and applications of artificial intelligence, providing them with basic knowledge and skills in the field of AI. Students will gain insights into machine learning, data processing, neural networks, and the ethical aspects of AI, and will be able to develop simple AI projects. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Can explain the fundamental concepts and history of artificial intelligence. |
PO-3 Follows current developments and practices for his/her profession and uses them effectively PO-4 Uses information technologies related to his/her profession (software, programs, animations, etc.) effectively |
Examination |
| LO-2 | Can apply machine learning and data processing techniques. |
PO-3 Follows current developments and practices for his/her profession and uses them effectively PO-11 Creates algorithms and data structures and performs mathematical calculations PO-14 Tests software and fixes bugs |
Examination |
| LO-3 | Can describe the basic structure and functioning of neural networks. |
PO-3 Follows current developments and practices for his/her profession and uses them effectively PO-4 Uses information technologies related to his/her profession (software, programs, animations, etc.) effectively PO-6 Effectively presents thoughts at the level of knowledge and skills through written and verbal communication and understandably expresses him/her PO-11 Creates algorithms and data structures and performs mathematical calculations PO-14 Tests software and fixes bugs |
Examination |
| LO-4 | Can evaluate the ethical and societal impacts of artificial intelligence. |
PO-3 Follows current developments and practices for his/her profession and uses them effectively PO-8 Has awareness of career management and lifelong learning PO-9 Has social, scientific, cultural, and ethical values in the stages of data collection, implementation, and dissemination of results related to his/her field |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| This course aims to introduce students to the fundamental concepts and techniques of artificial intelligence. Throughout the course, topics such as the history of AI, machine learning, data processing, neural networks, and the ethical aspects of AI will be covered. Students will reinforce their theoretical knowledge with practical applications and have the opportunity to develop simple AI projects. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to Artificial Intelligence | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 2 | History of Artificial Intelligence | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 3 | Basic Concepts | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 4 | What is Machine Learning? | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 5 | Data Collection and Preparation | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 6 | Simple Algorithms | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 7 | Clustering Techniques | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 8 | mid-term exam | |
| 9 | Neural Networks | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 10 | Image Processing | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 11 | Natural Language Processing | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 12 | Model Evaluation | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 13 | AI Tools | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 14 | AI and Ethics | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 15 | AI Projects | Problem Solving Method, Individual Study Method, Question Answer, Discussion Method, Lecture Method, Observation |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Yılmaz, C. (2024). Yapay Zeka: Teori ve Uygulamalar. Nobel Akademik Yayıncılık. ISBN: 978-625-393-169-8. | |
| Required Course instruments and materials | ||
| Lecture notes, projection and computer | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 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 | 1 | 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 | 0 | ||
| b) Search in internet/Library | 3 | 8 | 24 |
| 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 | 1 | 7 | 7 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 2 | 14 | 28 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 89 | ||