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Course Catalog
Web: http://www.ktu.edu.tr/ybs
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FACULTY of ECONOMICS and ADMINISTRATIVE SCIENCES / DEPARTMENT of MANAGEMENT INFORMATION SYSTEMS /
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YBS4011Artificial Intelligence3+0+0ECTS:4
Year / SemesterFall Semester
Level of CourseFirst Cycle
Status Elective
DepartmentDEPARTMENT of MANAGEMENT INFORMATION SYSTEMS
Prerequisites and co-requisitesNone
Mode of Delivery
Contact Hours14 weeks - 3 hours of lectures per week
LecturerDr. Öğr. Üyesi Fatih GÜRCAN
Co-Lecturer
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
The aim of this course is to introduce students to the field of artificial intelligence by explaining the basic principles and methods of artificial intelligence. Successful students will be able to analyze problems to determine where AI techniques can be applied and have the necessary skills to implement AI solutions. Topics covered will include the origins and evolution of artificial intelligence, its goals and the methods used to achieve them, and current applications.
 
Learning OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : To master the basic problems, applications and solution techniques related to Machine Learning and Deep Learning2,4,8,10
LO - 2 : Be able to make models of decision-making problems under uncertain state2,4,8,10
LO - 3 : To know how to use fully connected and consecutive artificial neural networks for reinforcing learning and designing it2,4,8,10
LO - 4 : To know optimization and exploring strategies for training of learning algorithms.2,4,8,10
LO - 5 : To implement learning applications on computer environment2,4,8,10
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

 
Contents of the Course
Introduction to artificial intelligence, Natural and Artificial Intelligence, Turing Test, Search methods, Planning, Intuitive Problem Solving, Machine Learning, Clustering, Classification and Regression Techniques, Deep Learning, Genetic Algorithms, Fuzzy Logic, Expert Systems, Artificial Intelligence Applications.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Introduction to artificial intelligence
 Week 2History and foundations of artificial intelligence
 Week 3Intelligent agents
 Week 4Problem solving
 Week 5Introduction to machine learning
 Week 6Cluster analysis and techniques
 Week 7Classification analysis and techniques
 Week 8Regression analysis and techniques
 Week 9Midterm Exam
 Week 10Fuzzy Logic
 Week 11Introduction to deep learning
 Week 12Artificial neural networks
 Week 13Convolutional Neural Networks
 Week 14Recurring neural networks
 Week 15Artificial intelligence and Business applications
 Week 16Final Exam
 
Textbook / Material
1Vasif Nabiyev 2012, Yapay Zeka, 5. Baskı, Seçkin Yayınevi, Trabzon
 
Recommended Reading
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 11/2023 1 50
End-of-term exam 16 01/2024 1 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 1 8 8
Laboratuar çalışması 1 8 8
Arasınav için hazırlık 2 8 16
Arasınav 1 1 1
Uygulama 2 14 28
Proje 1 10 10
Dönem sonu sınavı için hazırlık 2 14 28
Dönem sonu sınavı 1 1 1
Total work load142