GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
DEEP NEURAL NETWORKS AND APPLICATIONS/5251333
Course Title: DEEP NEURAL NETWORKS AND APPLICATIONS
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
1. Describe Image Classification, Loss Functions and Optimization
2. Review Neural Networks and Training Neural Networks
3. Apply Convolutional Neural Networks, Deep Learning Software, and CNN Architectures
4. Apply and Discuss Recurrent Neural Networks

 -- MODE OF DELIVERY
  Face to Face
 --WEEKLY SCHEDULE
1. Week  1. Computer Vision Overview
2. Week  2. Image Classification
3. Week  3. Loss Functions and Optimization
4. Week  4. Introduction to Neural Networks
5. Week  5. Convolutional Neural Networks and Applications
6. Week  6. Training Neural Networks
7. Week  7. Deep Learning Software and Applications
8. Week  8. Midterm Exam. CNN Architectures
9. Week  9. CNN Architectures
10. Week  10. Recurrent Neural Networks and Applications
11. Week  11. Detection and Segmentation
12. Week  12. Application Project Presentations
13. Week  13. Application Project Presentations
14. Week  14. Application Project Presentations
15. Week  
16. Week  
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
30
 Assignment
0
0
 Application
0
0
 Projects
1
30
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
60
 Percentage of Final Exam to Total Score  
40
 -- 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
12
3
36
 Searching in Internet and Library
12
3
36
 Material Design and Implementation
0
 Report Preparing
2
15
30
 Preparing a Presentation
2
15
30
 Presentation
1
5
5
 Midterm Exam and Preperation for Midterm Exam
1
10
10
 Final Exam and Preperation for Final Exam
1
10
10
 Other (should be emphasized)
0
 TOTAL WORKLOAD: 
199
 TOTAL WORKLOAD / 25: 
7.96
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1X
2X
3X
4X
5X
6X
7X
8X
9X
10X
11X
12X
 -- NAME OF LECTURER(S)
   (Assoc.Prof.Dr. Necaattin BARIŞÇI)
 -- WEB SITE(S) OF LECTURER(S)
   (https://websitem.gazi.edu.tr/site/nbarisci)
 -- EMAIL(S) OF LECTURER(S)
   (nbarisci@gazi.edu.tr)