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Course Information
Course Unit Title : Mechanical Systems Application of Neural Networks
Course Unit Code : 01MAK5191
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) Yapay zeka yaklaşımını algılayabilme
2) Learning strategies of the neural network
3) ANN modelling of the problems
4) Solving the problems by ANN and congluding the results
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : This module is concerned main principals of the ANN, reduction the dimensions, decision functions, optimum decision making criteria, teaching algorithm, forward and backward feeding algorithm, teaching and non teaching algorithm, fuzzy classification.
Languages of Instruction : Turkish
Course Goals : 1. solving ANN problems
2.which problem can be modellin by ANN
3. modelling of ANN
Course Aims : The aim of this course is to ensure that the students will be introduced to learning and decision making abilities of the neural net works, solution capabilities of the neural net works to the problem
WorkPlacement   Not Available
Recommended or Required Reading
Textbook : ppt, course note
Additional Resources : 1. Omid Omidvar, Judith Dayhoff, " Neural Networks & Pattern Recognition", Academic Press, September 1997. 2. Carl G. Looney," Pattern Recognition Using Neural Networks : Theory & Algorithms for Engineers & Scientists ",Oxford University Press, January 1997. 3. Shigeo Abe, Pattern Classification: Neuro-Fuzzy Methods and Their Comparision, Springer Verlag, 2001. 4. Richard O. Duda, Pattern Classification, Wiley-Interscience, 2000. 5. Sankar K. Pal, Pattern Recognition: From Classical to Modern Approaches, World Scientific Pub. Co., 2001
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 5 70  
Homeworks :
3 15 45  
Presentation :
0 0 0  
Project :
0 0 0  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 13 13  
Finals :
1 15 15  
Workload Hour (30) :
30  
Total Work Charge / Hour :
185  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 100
Quiz :
0 0
Homework :
2 0
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
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