BAYBURT University Information Package / Course Catalogue

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Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İD111Artificial Intelligence for Sustainable DevelopmentElective116
Level of Course Unit
Second Cycle
Objectives of the Course
Artificial General Intelligence by giving structure and algorithms artificial intelligence applications.
Name of Lecturer(s)
-
Learning Outcomes
1grasp the structure of Artificial General Intelligence
2grasp the Artificial Neural Networks
3Expert Systems grasp
4grasp the Genetic Algorithms
5grasp the Fuzzy Propositional Logic
Mode of Delivery
Normal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
Basic concepts (search, problem solving, knowledge representation methods, planning, natural language processing), artificial neural networks, expert systems, genetic algorithms, fuzzy propositional logic.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Introduction to artificial intelligence
2Problem Solving, Natural Language Processing
3Knowledge Representation Methods
4You Planning, Search, Vision, Robotics,
5General introduction to Artificial Neural Networks
6Artificial Neural Networks (Multi-layer Sensors-Backpropagation)
7Artificial Neural Networks (LVQ Network)
8General introduction to expert Systems
9Expert Systems
10Expert systems example
11General introduction to genetic introduction to algorithms
12Example of genetic algorithms
13General introduction to fuzzy Propositional Logic
14Fuzzy propositional logic example
Recommended or Required Reading
Russell S., Norvig P., 2002, "Artificial Intelligence: A modern approach", Prentice Hall series in Artificial Intelligence, 2nd Edition Luger G.F., 2004, "Artificial Intelligence: Structures and Strategies for Complex Problem Solving", Addison-Wesley, 5th Edition Uzman sistemler: Bir Yapay Zeka Uygulaması, Novruz Allahverdi, 2002 Kusiak, A., Intelligent Manufacturing Systems, Prentice Hall International Editions, New Jersey, 1990. Patterson D.W., 1990, "Introduction to artificial intelligence and expert systems", Prentice Hall
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Language of Instruction
Turkish
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination24896
Final Examination224
Individual Study for Mid term Examination21428
Individual Study for Final Examination14456
TOTAL WORKLOAD (hours)184
Contribution of Learning Outcomes to Programme Outcomes
PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
PO
7
LO11211111
LO21111111
LO31111111
LO41111111
LO51111111
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High