| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Deep Learning | YZM415 | Elective | Bachelor's degree | 4 | Fall | 5 |
Name of Lecturer(s)
Assistant Prof. Dr. Yasemin GÜLTEPE
Assistant Prof. Dr. İrfan KÖSESOY
Learning Outcomes of the Course Unit
1) Use machine learning tools
2) Use known machine learning algorithms to solve given problems
3) To be able to improve the knowledge learned in the course and offer new solutions unique to the problems
4) Re-arrange and optimize machine learning models.
5) Gain knowledge and skills to apply deep learning methods (such as computer vision, natural language processing, and big data) to fields.
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | Middle | Middle | Middle | Middle | Low | Low | Low | High | Low | Low | High | Low | |
| 2 | High | High | Middle | Middle | Low | Low | Middle | High | Middle | Low | Middle | Low | |
| 3 | High | High | Middle | Middle | Low | Low | Middle | High | Middle | Low | Middle | Low | |
| 4 | High | Middle | Middle | Middle | High | Middle | Middle | Middle | High | Middle | Middle | Low | |
| 5 | High | Middle | Middle | High | High | High | Middle | Middle | High | Middle | Middle | Middle | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Fundamentals of Linear Algebra and Probability, Introduction to Machine Learning, Deep Learning Tools, Convolutional Neural Networks, Recurrent Neural Networks, Deep Productive Models, Auto Coders, Deep Recursive Learning, Model Selection
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Language of Instruction
Turkish
Work Placement(s)
Not Required