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| YBS4011 | Artificial Intelligence | 3+0+0 | ECTS:4 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of MANAGEMENT INFORMATION SYSTEMS | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Fatih GÜRCAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to introduce students to the field of artificial intelligence by explaining the basic principles and methods of artificial intelligence. Successful students will be able to analyze problems to determine where AI techniques can be applied and have the necessary skills to implement AI solutions. Topics covered will include the origins and evolution of artificial intelligence, its goals and the methods used to achieve them, and current applications. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | To master the basic problems, applications and solution techniques related to Machine Learning and Deep Learning | 2,4,8,10 | | | LO - 2 : | Be able to make models of decision-making problems under uncertain state | 2,4,8,10 | | | LO - 3 : | To know how to use fully connected and consecutive artificial neural networks for reinforcing learning and designing it | 2,4,8,10 | | | LO - 4 : | To know optimization and exploring strategies for training of learning algorithms. | 2,4,8,10 | | | LO - 5 : | To implement learning applications on computer environment | 2,4,8,10 | | | 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 | | |
| Introduction to artificial intelligence, Natural and Artificial Intelligence, Turing Test, Search methods, Planning, Intuitive Problem Solving, Machine Learning, Clustering, Classification and Regression Techniques, Deep Learning, Genetic Algorithms, Fuzzy Logic, Expert Systems, Artificial Intelligence Applications. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to artificial intelligence | | | Week 2 | History and foundations of artificial intelligence | | | Week 3 | Intelligent agents | | | Week 4 | Problem solving | | | Week 5 | Introduction to machine learning | | | Week 6 | Cluster analysis and techniques | | | Week 7 | Classification analysis and techniques | | | Week 8 | Regression analysis and techniques | | | Week 9 | Midterm Exam | | | Week 10 | Fuzzy Logic | | | Week 11 | Introduction to deep learning | | | Week 12 | Artificial neural networks | | | Week 13 | Convolutional Neural Networks | | | Week 14 | Recurring neural networks | | | Week 15 | Artificial intelligence and Business applications | | | Week 16 | Final Exam | | | |
| 1 | Vasif Nabiyev 2012, Yapay Zeka, 5. Baskı, Seçkin Yayınevi, Trabzon | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 11/2023 | 1 | 50 | | End-of-term exam | 16 | 01/2024 | 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 | 3 | 14 | 42 | | Sınıf dışı çalışma | 1 | 8 | 8 | | Laboratuar çalışması | 1 | 8 | 8 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 1 | 1 | 1 | | Uygulama | 2 | 14 | 28 | | Proje | 1 | 10 | 10 | | Dönem sonu sınavı için hazırlık | 2 | 14 | 28 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 142 |
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