| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Deep Learning | MEH570 | Elective | Master's degree | 1 | Spring | 8 |
Name of Lecturer(s)
Prof. Dr. Ali TANGEL
Prof. Dr. Oğuzhan URHAN
Assistant Prof. Dr. Ayhan KÜÇÜKMANİSA
Learning Outcomes of the Course Unit
1) Can use deep learning tools
2) Can use deep learning algorithms to solve given problems
3) Improves the knowledge learned in the course and offer new solutions unique to the problems
4) Re-arranges and optimizes deep learning models
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
| Learning Outcomes | ||||||||
| 1 | No relation | High | No relation | No relation | No relation | No relation | No relation | |
| 2 | No relation | Middle | No relation | No relation | No relation | No relation | No relation | |
| 3 | No relation | High | No relation | No relation | No relation | No relation | No relation | |
| 4 | No relation | 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
Pattern Recognition
Course Contents
Fundamentals of Linear Algebra and Probability, Introduction to Machine Learning, Deep Learning Tools, Convolutional Neural Networks, Recurrent Neural Networks, Deep Generative Models, Auto-encoders, Deep Reinforcement Learning, Model Selection.
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