Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN429Data Mining Laboratory0+2+01412.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The subject matter of this course is to make the students practice the fundamentals of data mining.
Course Content Data Preprocessing, Visualization
Clustering
Association Analysis
Data Mining Applications and Tools
Course Methods and Techniques Lecture, Problem Solving, Drill and Practice, Preparing and/or Presenting Reports
Prerequisites and co-requisities ( BBM104 ) and ( BBM102 ) and ( AIN427 )
Course Coordinator None
Name of Lecturers Prof. Suat Özdemir
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar Data Mining: Concepts and Techniques, J. Han, M. Kamber, J. Pei Data Mining and Analysis, M. J. Zaki, W. Meira Jr.
Course Notes Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar

Data Mining: Concepts and Techniques, J. Han, M. Kamber, J. Pei

Data Mining and Analysis, M. J. Zaki, W. Meira Jr.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Practice 4 % 60
Final examination 4 % 40
Total
8
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 2 28
Hours for off-the-c.r.stud 14 2 28
Practice 4 15 60
General Exam Preparation 1 10 10
Total Work Load   Number of ECTS Credits 4,2 126

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 After completing the course, the students will practice the fundamentals of data mining through several applications Use data mining  tools Compare and contrast the various techniques
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Data Mining
2 Data Mining Tools
3 Data Preprocessing
4 Data Visualization
5 Classification : Decision Trees
6 Classification : SVM
7 Classification : Bayesian Classifiers
8 Association Analysis
9 Association Analysis for continuous and categorical data
10 Cluster Analysis : K-means, hierarchical clustering, DBSCAN
11 Cluster Analysis : fuzzy and probabilistic clustering
12 Cluster Analysis : SOM, graph-based
13 Anomaly Detection : statictical, distance-based
14 Anomaly Detection : density-based, clustering-based
15 Final exam preparation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 4 5 3 4 4 4 3 4 3 3 2 4
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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