SDU Education Information System
   Home   |  Login Türkçe  | English   
 
   
 
 


 
Course Information
Course Unit Title : The Applications of Artificial Intelligence
Course Unit Code : 01INS6143
Type of Course Unit : Compulsory
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. 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
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : the basic concepts and techniques of artificial intelligence, expert systems, rule-based systems, machine learning and artificial neural networks, genetic algorithms
Languages of Instruction : Turkish
Course Goals : To realize general structure of artificial intelligence, artificial neural networks, expert systems, genetic algorithms, fuzzy logic and to practise applications of this methods.
Course Aims : to simulate the intelligence with softwares or integrated
WorkPlacement   To simulate human intelligence and to provide modeling skills
Recommended or Required Reading
Textbook : 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
Additional Resources : -
Material Sharing
Documents : -
Assignments : 1 - A field study on Artificial Neural Networks
2 - A field study on Fuzzy Logic
Exams : Midterm Exam
Final exam
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 2 28  
Homeworks :
2 5 10  
Presentation :
1 4 4  
Project :
0 0 0  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 2 2  
Finals :
1 2 2  
Workload Hour (30) :
30  
Total Work Charge / Hour :
0  
Course's ECTS Credit :
0      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 50
Quiz :
0 0
Homework :
2 50
Attendance :
0 0
Application :
0 0
Lab :
0 0
Project :
0 0
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
100
The ratio of the term to success :
40
The ratio of final to success :
60
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 The introduction of artificial intelligence
  Study Materials: There is not.
2 To solve problem, processing of natural language
  Study Materials: A repeat of the previous week topic
3 The knowledge representation methods

  Study Materials: A repeat of the previous week topic
4 The planning, research, vision, agent

  Study Materials: A repeat of the previous week topic
5 The introduction to Neural Networks

  Study Materials: A repeat of the previous week topic
6 The Artificial Neural Networks (Multilayer Perceptron-Backpropagation )
  Study Materials: A repeat of the previous week topic
7 The Artificial Neural Networks (LVQ Network)
  Study Materials: A repeat of the previous week topic
8 The introduction to Expert Systems

  Study Materials: A repeat of the previous week topic
9 The Expert Systems

  Study Materials: A repeat of the previous week topic
10 The examples of Expert Systems

  Study Materials: A repeat of the previous week topic
11 The Genetic Algorithms input

  Study Materials: A repeat of the previous week topic
12 The example of Genetic Algorithms

  Study Materials: A repeat of the previous week topic
13 The introduction to Fuzzy Logic

  Study Materials: A repeat of the previous week topic
14 The example of Fuzzy Logic
  Study Materials: A repeat of the previous week topic
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0
 
0