Akademik Dersler - Tanıtım

Course Title	Code	Compulsory/Elective	Laboratory + Practice	ECTS
Data Mining	BİL524	II. SEMESTER	3+0	6.0

Language of Instruction	Türkçe
Course Type	Elective Courses
Course Instructor(s)	ARAŞ. GÖR. İSMAİL YENİLMEZ
Mode of Delivery	Distance Education
Prerequisites	There is no prerequisite or co-requisite for this course.
Courses Recomended	There is no other recommended course prior to this course.
Recommended Reading List	Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan kaufmann.
Assessment methods and criteria	Assignments will be given each week. In addition, written attendance and practice will be given as midterm and final exams. The total of the Assignment will be 30 percent, the midterm exam will be 30 percent and the final exam will be 40 percent.
Work Placement	This course has no internship practice.
Catalog Content	Introduction to Data Mining: Concepts and applications, Process and methodologies, Ethical considerations; RapidMiner/Knime Platforms: Overview of the platforms, Data preprocessing and modeling capabilities, Key features and functionalities, Strengths and weaknesses comparison, Cases and applications; Association Rule Mining: Apriori algorithm and variations, Evaluation metrics; Cluster Analysis: Requirements, Clustering methods and algorithms, Evaluation metrics for clustering; Classification: Classification algorithms, Evaluation metrics for classification performance; Neural Networks and Deep Learning: Artificial neural networks, Deep learning architectures.