Akademik Dersler - Tanıtım

Course Introduction Information


Code - Course Title	YZM4001 - Graduation Project I
Course Type	Required Courses
Language of Instruction	İngilizce
Laboratory + Practice	0+4
ECTS	5.0
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery	Face-to-Face / Project-Based Learning, Weekly Advisor Meetings, and Independent Study
Prerequisites	No formal prerequisites. (However, students are strongly recommended to have successfully completed foundational programming, mathematics, and artificial intelligence courses up to the end of the 4th semester.)
Courses Recomended	Algorithm Design and Analysis, Principles of Artificial Intelligence, Statistics, Data Acquisition and Processing, Introduction to Optimization Theory, Fundamentals of Supervised Learning, Artificial Neural Networks, Deep Learning, Applied Machine Learning, Introduction to Image Processing, Natural Language Processing.
Required or Recommended Resources	Current international academic papers (IEEE, ACM, Springer) depending on the nature of the selected project topic (Computer Vision, NLP, Data Analytics, etc.), technical documentations, library manuals, and relevant literature.
Recommended Reading List	Departmental Graduation Project Principles and Writing Manual.Current literature from Google Scholar, IEEE Xplore, and ScienceDirect databases.Official documentations of Python Data Science and AI libraries (NumPy, SciPy, PyTorch, TensorFlow, Hugging Face).
Assessment methods and criteria	In-Term Studies / Advisor EvaluationInterim Report / Project Proposal FileEnd-of-Term Project Report & Jury Presentation
Work Placement	There is no mandatory external work placement within the scope of this course. However, if the project is carried out jointly with an industrial partner (R&D Center, Technopark company, or industry-oriented projects like TUBITAK 2209-B), industrial application/fieldwork can be conducted.
Sustainability Development Goals

Content

Weeks	Topics
Week - 1	Determination of project topic, scope, and objectives.
Week - 2	Creation of the project management plan and preparation of the timeline.
Week - 3	Initiation of literature review related to the selected AI/machine learning problem.
Week - 4	In-depth analysis of academic sources (IEEE, ACM, etc.) and similar state-of-the-art studies.
Week - 5	Analysis of project requirements; determination of hardware, software, library, and dataset needs.
Week - 6	Procurement, inspection, and exploratory data analysis of the dataset to be used in the project.
Week - 7	Determination of data preprocessing strategies and designing the data pipeline.
Week - 8	Preparation of the Interim Report: Documentation of the literature review and requirements analysis.
Week - 9	Theoretical design and mathematical formulation of the proposed AI/deep learning model architecture.
Week - 10	Selection of baseline models and determination of performance evaluation metrics.
Week - 11	Design of the prototype software/system architecture and structuring the modular code.
Week - 12	Commencement of the final graduation project report writing and technical documentation.
Week - 13	Review of project outcomes, finalizing the report, and preparation for presentation/poster.
Week - 14	End-of-Term Presentation: Defense of the developed project and final report before the jury.

Learning Activities and Teaching Methods

Teaching Methods
Discussion
Question & Answer
Problem Solving
Report Preparation and/or Presentation
Proje Design/Management
Competences
Rational
Questoning
Follow ethical and moral rules
Effective use of a foreign language
Use time effectively
Information Management
To work autonomously
Organization and planning
Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)		0