Department of Industrial Engineering

IE 343 | Course Introduction and Application Information

Course Name

Data Mining

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
IE 343	SPRING	3	0	3	5

Prerequisites	Successfully completed IE 234 (with a minimum grade of DD) and IE 261 (with a minimum grade of DD) or successfully completed MATH 236 (with a minimum grade of DD).
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Dr. Öğr. Üyesi Kutluhan Erol
Course Lecturer(s)	Dr. Öğr. Üyesi Kutluhan Erol
Assistant(s)	-

Course Objectives

The main objective of this course is to provide fundamental knowledge about data mining methods and to enable students to use these methods with the help of data mining software tools. The course aims to focus on basic machine learning and data mining approaches.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Students will be able to analyse the fundamental characteristics of data mining.			X
LO2	Students will be able to explain the fundamental concepts related to classification problems.			X
LO3	Students will be able to analyse classification problems using appropriate solution methods.				X
LO4	Students will be able to develop descriptive and solution-oriented models for association and clustering problems.				X
LO5	Students will be able to solve data mining problems using the Python programming language.				X

Course Description

The course topics include methods and principles of machine learning and data mining.

Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction to Data Mining	Lecture Notes	LO1
2	Data Preprocessing, Data Types, Data Preparation	Lecture Notes	LO1
3	Data Preprocessing, Data Types, Data Preparation	Lecture Notes	LO1
4	Classification: Basic Concepts and Techniques	Lecture Notes	LO2
5	Classification: Overfitting	Lecture Notes	LO2
6	Classification: Rule-Based Classifiers, Nearest Neighbor Classifiers	Lecture Notes	LO3
7	Classification: Bayesian Classifiers, Artificial Neural Networks	Lecture Notes	LO3
8	Midterm		LO3
9	Classification: Support Vector Machines, Ensemble Methods	Lecture Notes	LO3
10	Association Analysis: Basic Concepts and Algorithms	Lecture Notes	LO4
11	Association Analysis: Advanced Concepts	Lecture Notes	LO4
12	Clustering Analysis: Basic Concepts and Algorithms	Lecture Notes	LO4
13	Clustering Analysis: Additional Topics and Algorithms	Lecture Notes	LO5
14	Anomaly Detection	Lecture Notes	LO5
15	Review	Lecture Notes	LO5
16	Final Exam		LO5

Course Notes/Textbooks	Witten Ian H. Eibe Frank and A. Mark. "Hall and Christopher J Pal. 2016. Data Mining Practical machine learning tools and techniques." ISBN 978-0128042915
Suggested Readings/Materials	-

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	3	15	X	X
Project	1	25			X	X	X
Midterm	1	30	X	X	X
Final Exam	1	30		X	X	X	X
Total	6	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	3	42
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	1	15	15
Presentation / Jury	-	-	-
Project	1	25	25
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	10	10
Final Exam	1	10	10
		Total	150