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Course Information
| Course Unit Title |
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The Applications of Artificial Intelligence |
| Course Unit Code |
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01INS6143 |
| Type of Course Unit |
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Compulsory |
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Level of Course Unit
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Second Cycle |
| Year of Study
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Preb |
| Semester
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255.Semester |
| Number of ECTS Credits Allocated
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6,00 |
| Name of Lecturer(s) |
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---
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| Course Assistants |
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| Learning Outcomes of The Course Unit |
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1. To uderstand general structure of Artificial intelligence
2. To learn artificial neural Networks
3. To learn expert systems
4. To learn genetic algorithms
5. To learn fuzzy logic
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| Mode of Delivery |
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Face-To-Face
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| Prerequisities and Co-requisities Courses |
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Unavailable
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| Recommended Optional Programme Components |
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Unavailable
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| Course Contents |
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the basic concepts and techniques of artificial intelligence, expert systems, rule-based systems, machine learning and artificial neural networks, genetic algorithms
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Languages of Instruction |
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Turkish
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| Course Goals |
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To realize general structure of artificial intelligence, artificial neural networks, expert systems, genetic algorithms, fuzzy logic and to practise applications of this methods.
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Course Aims |
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to simulate the intelligence with softwares or integrated
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WorkPlacement |
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To simulate human intelligence and to provide modeling skills
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Recommended or Required Reading
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Textbook
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1. The Applications of Artificial Intelligence, Çetin Elmas, Seçkin Yayınları, Ankara, 2007
2. The principles of Artificial Neural Networks, Zekai Şen, Su Vakfı Yayınları, İstanbul, 2004
3. The Principles of Fuzzy Logic and Modelling, Zekai Şen, Bilge Kültür Sanat, İstanbul, 2001
4. The Genetic Algorithms and The Optimization Methods, Zekai Şen, Su Vakfı Yayınları, İstanbul, 2004
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Additional Resources
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Material Sharing
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Documents
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-
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Assignments
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1 - A field study on Artificial Neural Networks
2 - A field study on Fuzzy Logic
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Exams
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Midterm Exam
Final exam
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Additional Material
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-
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Planned Learning Activities and Teaching Methods
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Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
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ECTS / Table Of Workload (Number of ECTS credits allocated)
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Student workload surveys utilized to determine ECTS credits.
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Activity
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Course Duration (Excluding Exam Week)
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Time Of Studying Out Of Class
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Homeworks
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Presentation
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Project
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Lab Study
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Field Study
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Visas
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Finals
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Workload Hour (30)
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Total Work Charge / Hour
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Course's ECTS Credit
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Assessment Methods and Criteria
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Studies During Halfterm
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Visa
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Quiz
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Homework
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Attendance
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Application
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Lab
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Project
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Workshop
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Seminary
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Field study
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TOTAL
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The ratio of the term to success
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The ratio of final to success
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TOTAL
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Weekly Detailed Course Content
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Week
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Topics
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1
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The introduction of artificial intelligence
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Study Materials: There is not.
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2
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To solve problem, processing of natural language
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Study Materials: A repeat of the previous week topic
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3
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The knowledge representation methods
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Study Materials: A repeat of the previous week topic
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4
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The planning, research, vision, agent
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Study Materials: A repeat of the previous week topic
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5
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The introduction to Neural Networks
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Study Materials: A repeat of the previous week topic
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6
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The Artificial Neural Networks (Multilayer Perceptron-Backpropagation )
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Study Materials: A repeat of the previous week topic
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7
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The Artificial Neural Networks (LVQ Network)
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Study Materials: A repeat of the previous week topic
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8
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The introduction to Expert Systems
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Study Materials: A repeat of the previous week topic
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9
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The Expert Systems
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Study Materials: A repeat of the previous week topic
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10
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The examples of Expert Systems
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Study Materials: A repeat of the previous week topic
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11
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The Genetic Algorithms input
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Study Materials: A repeat of the previous week topic
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12
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The example of Genetic Algorithms
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Study Materials: A repeat of the previous week topic
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13
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The introduction to Fuzzy Logic
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Study Materials: A repeat of the previous week topic
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14
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The example of Fuzzy Logic
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Study Materials: A repeat of the previous week topic
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