GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
PATTERN RECOGNETION BY ARTIFICIAL NETWORKS/5851306
Course Title: PATTERN RECOGNETION BY ARTIFICIAL NETWORKS
Credits 3 ECTS 7.5
Course Semester 1 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
Students who succeed this course: 1. Can explain the basic concepts artificial neural networks
2. Can use artificial neural network models
3. Can develop a project on pattern recognition using artificial neural networks
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  Introduction to Neural Networks, Artificial Neural Network Structure and Basic Elements
2. Week  First Artificial Neural Networks
3. Week  Artificial Neural Network Model (teacher learning)-Multilayer Perceptron
4. Week  Artificial Neural Network Model (Supportive Learning) - LVQ Model
5. Week  Sample Application of LVQ(Pattern Recognition)
6. Week  Artificial Neural Network Model (Unsupervised learning)
7. Week  Adaptive Resonance Theory (ART) Networks
8. Week  Sample Application of ART(Group Technology-Based Manufacturing Practice)
9. Week  Recurrent Networks
10. Week  ELMAN Networks
11. Week  Applications of Artificial Neural Networks
12. Week  Applications of Artificial Neural Networks
13. Week  Applications of Artificial Neural Networks
14. Week  Applications of Artificial Neural Networks
15. Week  
16. Week  
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
20
 Assignment
3
10
 Application
1
10
 Projects
0
0
 Practice
1
10
 Quiz
0
0
 Percent of In-term Studies  
50
 Percentage of Final Exam to Total Score  
50
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
 Reading Tasks
14
3
42
 Searching in Internet and Library
14
3
42
 Material Design and Implementation
7
3
21
 Report Preparing
6
4
24
 Preparing a Presentation
0
 Presentation
0
 Midterm Exam and Preperation for Midterm Exam
1
3
3
 Final Exam and Preperation for Final Exam
1
3
3
 Other (should be emphasized)
0
 TOTAL WORKLOAD: 
177
 TOTAL WORKLOAD / 25: 
7.08
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Attains knowledge through wide and in-depth investigations his/her field and surveys, evaluates, interprets, and applies the knowledge thus acquired.X
2Has a critical and comprehensive knowledge of contemporary engineering techniques and methods of application.X
3By using unfamiliar, ambiguous, or incompletely defined data, completes and utilizes the required knowledge by scientific methods; is able to fuse and make use of knowledge from different disciplines.X
4Has the awareness of new and emerging technologies in his/her branch of engineering profession, studies and learns these when needed.X
5Defines and formulates problems in his/her branch of engineering, develops methods of solution, and applies innovative methods of solution.X
6Devises new and/or original ideas and methods; designs complex systems and processes and proposes innovative/alternative solutions for their designX
7Has the ability to design and conduct theoretical, experimental, and model-based investigations; is able to use judgment to solve complex problems that may be faced in this process.X
8Has the oral and written communication skills in one foreign language at the B2 general level of European Language Portfolio.X
9Systematic and clear verbal or written transfer of the process and results of studies at national and international environmentsX
10Can present the progress and the results of his investigations clearly and systematically in national or international contexts both orally and in writingX
11Knows social, environmental, health, safety, and legal dimensions of engineering applications as well as project management and business practices; and is aware of the limitations and the responsibilities these impose on engineering practices.X
12Commits to social, scientific, and professional ethics during data acquisition, interpretation, and publication as well as in all professional activitiesX
 -- NAME OF LECTURER(S)
   (Prof. Dr. Fırat Hardalaç)
 -- WEB SITE(S) OF LECTURER(S)
   (https://websitem.gazi.edu.tr/site/firat)
 -- EMAIL(S) OF LECTURER(S)
   (firat@gazi.edu.tr)