SDU Education Information System
   Home   |  Login Türkçe  | English   
 
   
 
 


 
Course Information
Course Unit Title : Fuzzy Sets and Artificial Neural Networks
Course Unit Code : 01END5104
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : Preb
Semester : 255.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) : ---
Course Assistants :
Learning Outcomes of The Course Unit : 1) Understanding general structure of artificial intelligence
2) Understanding artificial neural networks
3) Understanding expert systems
4) Understanding genetic algorithms
5) Understanding fuzzy logic
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : Basic concepts (search, problem solving, knowledge representation methods, planning, natural language processing), artificial neural networks, expert systems, genetic algorithms, fuzzy logic.
Languages of Instruction : Turkish
Course Goals : Artificial intelligence applications to learning
Course Aims : Artificial intelligence applications, artificial intelligence to teach by giving the general structure and algorithms.
WorkPlacement   Not Available
Recommended or Required Reading
Textbook : Lecture Notes
Additional Resources : - Şen, Z., Bulanık Mantık İlkeleri ve Modelleme, Su Vakfı, 2009. - Öztemel, E., Yapay Sinir Ağları, Papatya Yayıncılık, 2003.
Material Sharing
Documents :
Assignments :
Exams :
Additional Material :
Planned Learning Activities and Teaching Methods
Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
ECTS / Table Of Workload (Number of ECTS credits allocated)
Student workload surveys utilized to determine ECTS credits.
Activity :
Number Duration Total  
Course Duration (Excluding Exam Week) :
14 3 42  
Time Of Studying Out Of Class :
14 4 56  
Homeworks :
6 3 18  
Presentation :
0 0 0  
Project :
1 10 10  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 20 20  
Finals :
1 30 30  
Workload Hour (30) :
30  
Total Work Charge / Hour :
176  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 30
Quiz :
0 0
Homework :
0 0
Attendance :
0 0
Application :
0 0
Lab :
0 0
Project :
1 30
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
60
The ratio of the term to success :
60
The ratio of final to success :
40
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 Uncertainity Basics
 
2 Membership Functions
 
3 Clasic and Fuzzy Sets
 
4 Fuzzy Sets Operations
 
5 Fuzzy Rule Base
 
6 Fuzzy Inference Base
 
7 Fuzzy Applications
 
8 Introduction to Artificial Neural Networks
 
9 Elements and Structre of Artificial Neural Networks
 
10 Elements and Structre of Artificial Neural Networks
 
11 Artificial Neural Networks Model: Multi Layer Perception
 
12 Artificial Neural Networks Model: Multi Layer Perception
 
13 Artificial Neural Networks Applications
 
14 Artificial Neural Networks Applications