Endüstri Mühendisliği

IE 343 | Ders Tanıtım Bilgileri

Dersin Adı

Data Mining

Kodu	Yarıyıl	Teori (saat/hafta)	Uygulama/Lab (saat/hafta)	Yerel Kredi	AKTS
IE 343	SPRING	3	0	3	5

Ön-Koşul(lar)	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).
Dersin Dili	English
Dersin Türü	ELECTIVE_COURSE
Dersin Düzeyi	Lisans
Dersin Veriliş Şekli	Face-To-Face
Dersin Öğretim Yöntem ve Teknikleri	Lecture / Presentation
Ulusal Meslek Sınıflandırma Kodu	-
Dersin Koordinatörü	Dr. Öğr. Üyesi Kutluhan Erol
Öğretim Eleman(lar)ı	Dr. Öğr. Üyesi Kutluhan Erol
Yardımcı(ları)	-

Dersin Amacı

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.

Öğrenme Çıktıları

Bu dersi başarıyla tamamlayabilen öğrenciler;

Ad	Açıklama	PC Alt	* Katkı Düzeyi
Ad	Açıklama	PC Alt	1	2	3	4	5
LO1	Students will be able to analyse the fundamental characteristics of data mining.	1.4		X
LO2	Students will be able to explain the fundamental concepts related to classification problems.	1.5		X
LO3	Students will be able to analyse classification problems using appropriate solution methods.	2			X
LO4	Students will be able to develop descriptive and solution-oriented models for association and clustering problems.	3.1			X
LO5	Students will be able to solve data mining problems using the Python programming language.	4			X

Ders Tanımı

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

Dersin İlişkili Olduğu Sürdürülebilir Kalkınma Amaçları

Dersin Kategorisi	Temel Ders
	Uzmanlık/Alan Dersleri	X
	Destek Dersleri
	İletişim ve Yönetim Becerileri Dersleri
	Aktarılabilir Beceri Dersleri

HAFTALIK KONULAR VE İLGİLİ ÖN HAZIRLIK ÇALIŞMALARI

Hafta	Konular	Ön Hazırlık	Öğrenme Çıktısı
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

Ders Kitabı	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
Önerilen Okumalar/Materyaller	-

DEĞERLENDİRME ÖLÇÜTLERİ

Yarıyıl Aktiviteleri	Sayı	Katkı Payı %	LO1	LO2	LO3	LO4	LO5
Ödev	3	15	X	X
Proje	1	25			X	X	X
Ara Sınav	1	30	X	X	X
Final Sınavı	1	30		X	X	X	X
Toplam	6	100

AKTS / İŞ YÜKÜ TABLOSU

Yarıyıl Aktiviteleri	Sayı	Süre (Saat)	İş Yükü
Katılım	-	-	-
Teorik Ders Saati	16	3	48
Laboratuvar / Uygulama Ders Saati	-	-	-
Sınıf Dışı Ders Çalışması	14	3	42
Arazi Çalışması	-	-	-
Küçük Sınav / Stüdyo Kritiği	-	-	-
Portfolyo	-	-	-
Ödev	1	15	15
Sunum / Jüri Önünde Sunum	-	-	-
Proje	1	25	25
Seminer/Çalıştay	-	-	-
Sözlü Sınav	-	-	-
Ara Sınavlar	1	10	10
Final Sınavı	1	10	10
		Toplam	150

DERSİN ÖĞRENME ÇIKTILARININ PROGRAM YETERLİLİKLERİ İLE İLİŞKİSİ

#	PC Alt	Program Yeterlilikleri / Çıktıları	* Katkı Düzeyi
#	PC Alt	Program Yeterlilikleri / Çıktıları	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics
	2	Science
	3	Basic Engineering
	4	Computation		LO1
	5	Related engineering discipline-specific topics		LO2
	6	The ability to apply this knowledge to solve complex engineering problems
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.				LO3
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems			LO4
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.				LO5
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote or hybrid)
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
	1	Knowledge of business practices such as project management and economic feasibility analysis
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.