Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN427Introduction to Data Mining3+0+03612.08.2025

 
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 aim of this course is to teach the fundamentals of data processing and data mining.
Course Content Basic concepts in Data Mining
Data Preprocessing, Visualization, OLAP
Classification
Clustering
Association Analysis
Data Mining Applications and Tools
Course Methods and Techniques Lecture, Discussion, Question and Answer
Prerequisites and co-requisities ( BBM104 ) and ( BBM102 ) and ( AIN429 )
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
Midterm Exam 2 % 60
Final examination 1 % 40
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 5 70
Preparation for Midterm Exam 2 20 40
General Exam Preparation 1 30 30
Total Work Load   Number of ECTS Credits 6,06666666666667 182

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Students will learn basic concepts of data mining and how to preprocess data, get familiar with algorithms used for data mining.
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Data Mining
2 Data Prepocessing
3 Data Exploration
4 Classification : Basic Concepts, Decision Trees and Model Evaluation
5 Classification : Alternative Techniques
6 Midterm exam
7 Association Analysis : Basic Concepts and Algorithms
8 Association Analysis : Advanced Concepts
9 Cluster Analysis : Basic Concepts and Algorithms
10 Cluster Analysis : Additional Issues and Algorithms
11 Midterm exam
12 Anomaly Detection
13 Data Mining Applications
14 Data Mining Tools
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 4 3 4 4 1 1 3 3 2 2 3
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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