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| YZM3012 | Artificial Intelligence | 3+1+0 | ECTS:5 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures and 1 hour of practicals per week | | Lecturer | Prof. Dr. Hamdi Tolga KAHRAMAN | | Co-Lecturer | ASSOC. PROF. DR. Hamdi Tolga KAHRAMAN, | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to implement various Artificial Intelligence (AI) methodologies, coding of AI methods in different programming languages. Modeling of various engineering problems by using AI techniques. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | students learn the basic concepts of Artificial Intelligence | 1,4,8 | 1, | | LO - 2 : | students learn the Types and Applications of Artificial Intelligence | 1,4,8 | 1, | | LO - 3 : | students learn supervised learning methods | 1,4,8 | 1, | | LO - 4 : | students learn unsupervised learning methods | 1,4,8 | 1, | | LO - 5 : | students learn reinforcement learning methods | 1,4,8 | 1, | | LO - 6 : | students can develop the hybrid algorithms | 1,4,8 | 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 | | |
| Introduction to Artificial Intelligence (AI): Definition of Intelligence; Definition, Aims, Importance and Limits of AI.
Types and Applications of Artificial Intelligence
Classification Problems and Probabilistic Classification (Naive Bayes)
Classification Problems and Instance-based Classification (k-nn, decision trees)
Meta-heuristic search algorithms, genetic algorithm, artificial bee colony
Meta-heuristic search algorithms, symbiotic organism search
Estimation Problems and Algorithms
Artificial Neural Networks, Intuitive Prediction Algorithm
Coding of Artificial Neural Networks
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to AI | | | Week 2 | Artificial Intelligence Definition, History and Basics | | | Week 3 | Types and Applications of Artificial Intelligence | | | Week 4 | Classification Problems and Probabilistic Classification | | | Week 5 | Classification Problems and Instance-Based Classification (KNN, decision trees) | | | Week 6 | Clustering Algorithms | | | Week 7 | Heuristic Search Algorithms
Genetic Algorithm
| | | Week 8 | Heuristic Search Algorithms
Artificial Bee Colony Algorithm, Symbiosis Organism Search | | | Week 9 | Midterm exam | | | Week 10 | Estimation Problems and Algorithms
Artificial neural networks | | | Week 11 | Estimation Problems and Algorithms
Artificial neural networks
| | | Week 12 | Coding and Applying of Heuristic Estimation Algorithm in Engineering Problems | | | Week 13 | Coding and Applying of Artificial Neural Network Algorithm in Engineering Problems | | | Week 14 | Coding and Applying of Artificial Neural Network Algorithm in Engineering Problems | | | Week 15 | Deep Neural Networks | | | Week 16 | Final Exam | | | |
| 1 | Mitchell. T. M., Machine Learning, McGraw-Hill Science/Engineering/Math, 154-184, (1997). | | | 2 | Artificial Intelligence: Foundations of Computational Agents, David Poole, Alan Mackworth, Cambridge University Press 2010. | | | 3 | Introducing Artificial Intelligence, H. Brighton, H. Selina, Icon boks and totem boks, 2007. | | | |
| 1 | Mühendislikte Yapay Zeka Uygulamaları, Ufuk Kitabevi, Ağustos 2003. | | | 2 | Yapay Sinir Ağları, Çetin ELMAS, Seçkin Yayınları, Ankara, 2003 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Practice | 13 | | 1 | 20 | | End-of-term exam | 16 | | | 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 | 3 | 10 | 30 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 2 | 1 | 2 | | Uygulama | 1 | 14 | 14 | | Dönem sonu sınavı için hazırlık | 5 | 4 | 20 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 126 |
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