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| TBB5132 | Intoduction To Artifical Inteligence | 2+2+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 2 hours of lectures and 2 hours of practicals per week | | Lecturer | -- | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Artificial intelligence approaches, principle concepts, problems solution which requires searching process, information expression ways, learning algorithms and To gain information and ability about advanced artificial intelligence subjects. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | General view to artificial intelligence and implementation areas. | 2,3,4,5,6,7 | 1,3 | | PO - 2 : | Introduction to artificial neural networks, forming artificial neural networks and structures. | 2,7 | 1,3 | | PO - 3 : | Learning methods. | 2,7 | 1,3 | | PO - 4 : | Artificiai neural network implementations: Implementation areas. | 2,7 | 1,3 | | PO - 5 : | rtificial intelligence problems samples. | 2,7 | 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), PO : Learning Outcome | | |
| Introduction to artificial intelligence, problem solving methods, turing test, search methods, herustic analysis, games, alpha beta and min-max algorithms, logic representation, predicate computing, semantic networks, knowledge base, rules, natural language processing, introduction to genetic algorithms. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Intelligence, general knowledge about the concept of Artificial Intelligence. Turing test sample, Eliza, and other similar information about the software | | | Week 2 | Application areas of Artificial Intelligence, advantages, disadvantages are? Past and present studies related to Artificial Intelligence. | | | Week 3 | What is of intuitive? Of Artificial Intelligence and Intuitive relationship. Examples from daily life | | | Week 4 | Intuitive problem examples. Four horse problem solution. Practical problems and information about the Explorer Dealer problems. | | | Week 5 | Colors of the map with the solution to the problem of the heuristic algorithms. | | | Week 6 | What is cryptology? Concepts and algorithms used in cryptography detailed information. | | | Week 7 | General information about the Enigma. Caesar password and practical example. | | | Week 8 | Mid-term exam | | | Week 9 | General structure of expert systems. Today's examples of Expert Systems. Why expert systems are needed? | | | Week 10 | Expert systems and detailed information about the internal structure. | | | Week 11 | Statistical Approach, weather forecast based on probability samples. | | | Week 12 | Homework | | | Week 13 | Homework | | | Week 14 | Genetic algorithms. | | | Week 15 | Final exams. | | | Week 16 | End-of-term exam | | | |
| 1 | Nabiyev V.V. 2003, Yapay Zeka, Seçkin Yayıncılık, Ankara, 724 p. | | | |
| 1 | http://www.yapay-zeka.org/ | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | 08/04/2014 | 1,5 | 30 | | Homework/Assignment/Term-paper | 12 | 22/05/2014 | 2 | 20 | | End-of-term exam | 16 | 04/06/2014 | 1,5 | 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 | 13 | 65 | | Sınıf dışı çalışma | 2 | 8 | 16 | | Arasınav için hazırlık | 4 | 1 | 4 | | Arasınav | 1.5 | 1 | 1.5 | | Uygulama | 4 | 12 | 48 | | Ödev | 4 | 2 | 8 | | Dönem sonu sınavı için hazırlık | 6 | 1 | 6 | | Dönem sonu sınavı | 1.5 | 1 | 1.5 | | Total work load | | | 150 |
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