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., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
Assessment methods and criteria	Assignments will be given throughout the weeks. The midterm and final exams will be conducted in written form. The assignments will account for 30% of the total grade, the midterm exam for 30%, and the final exam for 40%.
Work Placement	This course has no internship practice.
Catalog Content	Introduction to Data Mining: Concepts and applications, Process and methodologies, Ethical considerations; R and Python Programming Languages: An Overview of IDEs, 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.