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SDÜ Education Information System Course Content
Programme
Graduate School of Natural and Applied Sciences Mechanical Engineering
Course Information
Course Unit Code
Course Unit Title
Credit Theoretic
Credit Pratic
Credit Lab/A
Credit Total
Credit Ects
Semester
01MAK5191
Mechanical Systems Application of Neural Networks
3.00
0.00
0.00
3.00
6.00
1
Course Information
Language of Instruction
Turkish
Type of Course Unit
Elective
Course Coordinator
Professor Cengiz KAYACAN
Course Instructors
1-M. Cengiz KAYACAN
Course Assistants
 
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
Course Goals
1. solving ANN problems
2.which problem can be modellin by ANN
3. modelling of ANN
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
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.
Prerequisities and Co-requisities Courses
 
Recommended Optional Programme Components
 
Mode Of Delivery
 
Level of Course Unit
 
Assessment Methods and Criteria
ECTS / Table Of Workload (Number of ECTS credits allocated)
Studies During Halfterm
Number
Co-Efficient
Activity
Number
Duration
Total
Visa
1
100
Course Duration (Excluding Exam Week)
14
3
42
Quiz
0
0
Time Of Studying Out Of Class
14
5
70
Homework
2
0
Homeworks
3
15
45
Attendance
0
0
Presentation
0
0
0
Application
0
0
Project
0
0
0
Lab
0
0
Lab Study
0
0
0
Project
0
0
Field Study
0
0
0
Workshop
0
0
Visas
1
13
13
Seminary
0
0
Finals
1
15
15
Field study
0
0
Workload Hour (30)
30
TOTAL
100
Total Work Charge / Hour
185
The ratio of the term to success
40
Course's ECTS Credit
6
The ratio of final to success
60
 
TOTAL
100
 
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)
Work Placements
As with any other educational component, credits for work placements are only awarded when the learning outcomes have been achieved and assessed. If a work placement is part of organised mobility (such as Farabi and Erasmus), the Learning Agreement for the placement should indicate the number of credits to be awarded if the expected learning outcomes are achieved.
Program Learning Outcomes
No
Course's Contribution to Program
Contribution
Course Content