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| BIL4013 | Artificial Intelligence | 3+0+0 | ECTS:4 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | -- | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to teach how to use the information that is obtained by students, in the applications and to teach the knowledge modeling. Main objective is to study basic concepts of artificial intelligence and to experiment with them in sample application domains by designing and implementing simple programmes and incrementally augmenting them with further concepts. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | compare artificial and natural intelligence and describe fundamental problems of artificial intelligence. | 2,3,4,12 | 1,3 | | LO - 2 : | prioritise between basic and heuristic search techniques.
| 2,3,4,12 | 1,3 | | LO - 3 : | describe knowledge modeling and determine how to programm it. | 2,3,4,12 | 1 | | LO - 4 : | determine how speech, natural languages, learning and other behavioural process is modelled with computer and determine how to apply fundamental approaches such as artificial neural networks and genetic algorithms on problems | 2,3,4,12 | 1,3 | | 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 | | |
| Can machines think? Turing's imitation game.Intelligent Agents. Basic search techniques. Problem Solving Languages of AI. Automated reasoning. Game Playing. Building Knowledge- based systems. Expert systems. Production systems. Frame Systems and Semantic Networks. Pattern recognition. Natural Language Processing. Artificial Neuron Network Applications |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction. Turing and Chinnes Room Tests. State Space. | | | Week 2 | Problem Solving Methods | | | Week 3 | Basic Search Techniques. Heuristic search | | | Week 4 | Game Programming. Alfa-beta pruning and minimax algorithm | | | Week 5 | Knowledge: facts and ruls. Building Knowledge Based Systems | | | Week 6 | Knowledge representation. Semantic net. Frame. Scene | | | Week 7 | Expert Systems. Production Systems | | | Week 8 | Mid-term exam | | | Week 9 | Pattern Recognition | | | Week 10 | Recognition of printed and hand-written characters | | | Week 11 | Biometric Recognition | | | Week 12 | midterm exam | | | Week 13 | Natural Language processing. Parsers | | | Week 14 | Learning. Artificial Network Applications | | | Week 15 | Speech Recognition. Voice analysis and Synthesis
| | | Week 16 | End-of-term exam | | | |
| 1 | Nabiyev V. V., 2005 Yapay Zeka: Problemler, Yöntemler, Algoritmalar, Ankara (2. Baskı) | | | 2 | Russell, Stuart J. ; Norvig, Peter, 2003 , Artificial Intelligence: A Modern Approach (2nd ed. ) | | | |
| 1 | Nilsson, Nils,1998 , Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | | 2 | 30 | | Quiz | 12 | | 2 | 20 | | End-of-term exam | 16 | | 2 | 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 | 4 | 11 | 44 | | Arasınav için hazırlık | 10 | 1 | 10 | | Arasınav | 2 | 1 | 2 | | Uygulama | 5 | 2 | 10 | | Kısa sınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 13 | 1 | 13 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 125 |
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