| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 1 | ECE661 | DEEP LEARNING | 3+0+0 | 3 | 7,5 |
|
Language of Instruction
|
English
|
|
Level of Course Unit
|
Master's Degree
|
|
Department / Program
|
ELECTRICAL AND COMPUTER ENGINEERING
|
|
Type of Program
|
Formal Education
|
|
Type of Course Unit
|
Elective
|
|
Course Delivery Method
|
Face To Face
|
|
Objectives of the Course
|
O1. Gain an understanding of deep learning architectures
O2. Learn the techniques used for developing deep learning models
O3. Develop skills for practical aspects of deep learning
|
|
Course Content
|
Regularization for deep learning
Optimization for training deep models
Convolutional networks
Recurrent and recursive networks
Practical methodology of deep learning
Deep learning applications
|
|
Course Methods and Techniques
|
|
|
Prerequisites and co-requisities
|
None
|
|
Course Coordinator
|
None
|
|
Name of Lecturers
|
None
|
|
Assistants
|
None
|
|
Work Placement(s)
|
No
|
Recommended or Required Reading
|
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
ECTS Allocated Based on Student Workload
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
Weekly Detailed Course Contents
Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77911&lang=en