|
|
| YZT2003 | Operational Artificial Intelligence | 3+1+0 | ECTS:5 | | Year / Semester | Fall Semester | | Level of Course | Short Cycle | | Status | Compulsory | | Department | Computer Technology | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures and 1 hour of practicals per week | | Lecturer | Öğr. Gör. Didem ÇAKIR | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The purpose of the Operational Artificial Intelligence course is to understand the use of artificial intelligence in operational processes, its impact on improving efficiency, and its implementation methods. The course aims to equip students with the skills to develop AI-based solutions for process optimization, data analytics, and decision support systems. It also seeks to raise awareness about the ethical and legal aspects of artificial intelligence. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Gains knowledge about production processes. | 4 | 1, | | LO - 2 : | Understands the relationship between artificial intelligence and other scientific disciplines. | 2,3 | 1, | | LO - 3 : | Acquires knowledge about artificial intelligence processes. | 6,7 | 1, | | LO - 4 : | Recognizes artificial intelligence systems used in production processes worldwide. | 2 | 1, | | LO - 5 : | Develops ideas on the use of artificial intelligence in production processes. | 2,4 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| It aims to provide knowledge and skills related to the integration of artificial intelligence into operational business processes, such as production and management stages. |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | What is Production Management? | | | Week 2 | Basic Concepts in Production Management | | | Week 3 | Production Processes and Analyses | | | Week 4 | The Relationship Between Data Science, Machine Learning, Deep Learning, and Artificial Intelligence | | | Week 5 | Machine Learning: Process, Applications, and Tools | | | Week 6 | Deep Learning | | | Week 7 | Natural Language Processing (NLP) | | | Week 8 | Programming Languages, Software Libraries, and Tools Used in AI Development | | | Week 9 | Midterm Exam | | | Week 10 | AI Processes: DevOps, MLOps, AIOps, LLMOps, ModelOps, DataOps | | | Week 11 | AI Processes: DevOps, MLOps, AIOps, LLMOps, ModelOps, DataOps (continued) | | | Week 12 | The AI Ecosystem Worldwide and Manufacturers Using AI | | | Week 13 | Ideas and Designs for the Use of AI in the Public Sector | | | Week 14 | AI Ideas and Applications in Production | | | Week 15 | AI Ideas and Applications in Production (continued) | | | Week 16 | For this course, the Midterm Exam will be conducted on a date between the 9th and 16th weeks. Following the exam, the topics will be postponed by one week. | | | |
| 1 | Özkan, M. (2021). Makine Öğrenmesine Giriş ve Uygulamalar. Kodlab Yayınları. | | | |
| 1 | Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education. | | | 2 | IBM. (2023). Operationalizing AI for Enterprises: Best Practices for Implementing AI in Business. IBM White Paper. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | 50 | | End-of-term exam | 16 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 5 | 14 | 70 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Arasınav için hazırlık | 1 | 14 | 14 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 2 | 14 | 28 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 170 |
|