TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PR. (ENGLISH) | |
| Code | YZZ211 |
| Name | Introduction to Data Mining |
| Term | 2026-2027 Academic Year |
| Semester | 3. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Label | FE Field Education Courses C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. YUSUF ALPER KAPLAN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The objective of this course is to introduce basic data mining algorithms.
Course Content
Introduction to data mining, data preprocessing, association rules, classification, clustering, outlier detection algorithms and their applications.
Course Precondition
None
Resources
Jiawei Han, Micheline Kamber and Jian Pei , Data Mining: Concepts and Techniques, 3rd edition, Morgan Koufmann,2011
Notes
Jiawei Han, Micheline Kamber and Jian Pei , Data Mining: Concepts and Techniques, 3rd edition, Morgan Koufmann,2011
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Comprehends the basic data mining algorithms. |
| LO02 | Applies data preprocessing, association rules, classification, clustering, outlier detection algortihms |
| LO03 | Applies data mining to solve up-to-date problems. |
| LO04 | Decides which data mining technique is to be applied in which case. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | It provides a broad range of knowledge about fundamental Computer Science concepts, algorithms and data structures. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learns basic computer topics such as software development, programming languages, and database management. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understands advanced computing fields such as data science, artificial intelligence, and machine learning. | 5 |
| PLO04 | - | Learn about topics such as computer networks, cyber security, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develops skills in designing, implementing and analyzing algorithms. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gains the ability to use different programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learns data analysis, database management and big data processing skills. | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Gains practical experience by working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthens collaboration and communication skills within the team. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | It provides a mindset open to technological innovations. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourages continuous learning and self-improvement competence. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Develops the ability to solve complex problems. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Definition of data mining, and steps of data mining process | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Data preprocessing steps | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Weka package | Reading the lecture notes and making practice | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Soru-Cevap |
| 4 | Association rule mining algorithms | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Performance improvements of association rule mining algortihms | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Basic classification algorithms (decision tree, Naive Bayes) | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Classifiers' performance evaluation techniques | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Reading the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Rule based classifiers, SVM and other classifiers | Reading the lecture notes and making practice | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Basic clustering algorithms (k means) | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Soru-Cevap |
| 11 | Basic clustering algorithms (hierarchical methods) | Reading the lecture notes | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 12 | Outlier detection methods | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Introduction to Web and text mining | Reading the lecture notes | Öğretim Yöntemleri: Soru-Cevap |
| 14 | Preparing the project presentations | Making practice, and preparing presentation | Öğretim Yöntemleri: Soru-Cevap, Grup Çalışması, Proje Temelli Öğrenme |
| 15 | Project presentations | Making practice, and preparing presentation | Ölçme Yöntemleri: Sözlü Sınav, Proje / Tasarım |
| 16 | Term Exams | Reading the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reading the lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||