| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Advances In Deep Learning | MEH150 | Elective | Master's degree | 1 | Spring | 8 |
Name of Lecturer(s)
Assistant Prof. Dr. Ayhan KÜÇÜKMANİSA
Learning Outcomes of the Course Unit
1) Use deep learning tools
2) Gain up-to-date knowledge on deep learning
3) Evaluate the advantages and disadvantages of deep learning methods
4) To be able to improve the knowledge learned in the course and offer new solutions unique to the problems
5) Re-arrange and optimize deep learning models
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
| Learning Outcomes | ||||||||
| 1 | High | High | No relation | No relation | No relation | No relation | No relation | |
| 2 | High | High | No relation | No relation | No relation | No relation | No relation | |
| 3 | High | High | No relation | No relation | No relation | No relation | No relation | |
| 4 | High | High | No relation | No relation | No relation | No relation | No relation | |
| 5 | High | High | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Deep Learning
Course Contents
Deep Learning Tools, Semi-Supervised Learning, Active Learning, Continual Learning, Few-Shot Learning.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Project to Course Grade |
70% |
|---|---|
Contribution of Final Examination to Course Grade |
30% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required