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| YZT1007 | Introduction to Artificial Intelligence | 3+0+0 | ECTS:4 | | 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 per week | | Lecturer | Öğr. Gör. Didem ÇAKIR | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | |
The Introduction to Artificial Intelligence course aims to introduce students to the fundamental principles, historical development, and application areas of artificial intelligence. Additionally, it seeks to equip students with basic technical knowledge and skills, fostering analytical thinking and problem-solving abilities in AI applications. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Knows the fundamental concepts of artificial intelligence. | 1,2 | 1, | | LO - 2 : | Learns widely used artificial intelligence techniques and their importance. | 2 | 1, | | LO - 3 : | Gains knowledge about the programming languages, software, tools, and processes required for developing artificial intelligence. | 4,5 | 1, | | LO - 4 : | Understands the subfields of artificial intelligence, its products, and real-world applications. | 3,5 | 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 | | |
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The fundamental concepts and methods of artificial intelligence. Problem-solving using artificial intelligence; search methods with and without problem-specific knowledge. Local search methods and simulated annealing algorithms. Meta-heuristic algorithms. Introduction to artificial neural networks. Game problems. The Prolog programming language, knowledge representation, and logical reasoning. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic Concepts, History, and Philosophy of Artificial Intelligence | | | Week 2 | Intelligent Agents | | | Week 3 | Problem Solving with Artificial Intelligence and Introduction to Search Algorithms | | | Week 4 | Search Algorithms Without Problem-Specific Knowledge | | | Week 5 | Heuristic Algorithms and Search | | | Week 6 | Game Problems | | | Week 7 | Meta-Heuristic Search Methods | | | Week 8 | Artificial Neural Networks | | | Week 9 | Midterm Exam | | | Week 10 | Knowledge-Based Agents | | | Week 11 | Machine Learning: Inductive Learning, Learning by Commands, Learning by Examples | | | Week 12 | Inference in First-Order Logic | | | Week 13 | Student Project Presentations | | | Week 14 | Student Project Presentations | | | Week 15 | Student Project Presentations | | | Week 16 | For this course, the Midterm Exam will be conducted on a date between the 7th and 15th weeks. Following the exam, the topics will be postponed by one week. | | | |
| 1 | Güven, F. (2021). Veri Bilimine Giriş ve Python ile Uygulamalar. Ankara: Akademik Yayınları. | | | 2 | Mehmet Özkan, Makine Öğrenmesine Giriş ve Uygulamalar, Kodlab. | | | |
| 1 | Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson. | | | 2 | Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press. | | | |
| 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 | 3 | 14 | 42 | | Arasınav için hazırlık | 1 | 14 | 14 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 3 | 14 | 42 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 170 |
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