Akademik Dersler - Tanıtım

Last Updated: 03/10/2025

Code - Course Title	Compulsory/Elective	Laboratory + Practice	ECTS
BVA202 - Feature Engineering	IV. SEMESTER	2 +1	5.0

Language of Instruction	Türkçe
Course Type	Required Courses
Course Instructor(s)	PROF. DR. ALPER TOLGA KUMTEPE
Mode of Delivery	Face to face
Place of Delivery
Used Educational Platforms
Prerequisites	Basic Mathematical Knowledge
Courses Recomended	Basic Mathematical Knowledge
Recommended Resources	Faculty Recommendation
Work Placement	N/A
Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		Introduction to the course and feature engineering.
Week - 2		Handling missing values and imputation techniques are introduced.
Week - 3		Different encoding methods for categorical variables are covered.
Week - 4		Scaling and normalization of numerical data are applied.
Week - 5		Data transformations and interaction-based feature creation are practiced.
Week - 6		Feature extraction from temporal and time-series data is performed.
Week - 7		Numerical features are extracted from text data.
Week - 8		Basic feature selection methods are introduced.
Week - 9		Advanced feature selection methods are applied.
Week - 10		Dimensionality reduction techniques such as PCA are explained.
Week - 11		Visualization and dimensionality reduction with t-SNE and UMAP are performed.
Week - 12		Automated feature engineering tools are introduced.
Week - 13		A hands-on project is conducted on large datasets.
Week - 14		Project presentations and overall evaluation are carried out.

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Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
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