FACULTY OF ENGINEERING

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
Fall/Spring
3
0
3
5

Prerequisites
  IE 234 To succeed (To get a grade of at least DD)
and IE 261 To succeed (To get a grade of at least DD)
or MATH 236 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives The main objective of this course is to provide a basic understanding of data mining concepts and to use it in data mining software packages, especially in Weka. The course will cover basic approaches in machine learning and data mining.
Learning Outcomes The students who succeeded in this course;
  • open data files and inspect basic characteristics of the data using Explorer panel in Weka.
  • solve Classification problems by using the Classifiers in Weka and interpret the output.
  • filter and visualize the data.
  • explain Naive Bayes, ZeroR, OneR and Nearest Neighbor.
  • apply supervised learning models such as linear regression, logistic regression and support-vector machines.
Course Description The topics include basic machine learning and data mining methods and principles.

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction to Data Mining, Weka Software Lecture Slides
2 Weka Installation, Loading and Displaying data, Classification, Creating a Classifier Lecture Slides
3 Using Filters, Visualizing Data Lecture Slides
4 Evaluating Classifiers, Baseline Accuracy Lecture Slides
5 1. Midterm
6 Cross Validation Lecture Slides
7 Simple Classifiers, Overfitting Lecture Slides
8 Using Probabilities, Decision Trees Lecture Slides
9 Nearest Neighbor Algorithm, Using Weka in practice Lecture Slides
10 2. Midterm
11 Classification Boundaries, Linear Regression Lecture Slides
12 Classification with Regression, Logistic Regression Lecture Slides
13 Support Vector Machines, Ensemble Learning Lecture Slides
14 Data Mining Process, Pitfalls and Pratfalls, Data Mining and Ethics Lecture Slides
15 Review of the Semester
16 Final

 

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

Lecture Slides

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
2
60
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
2
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
2
15
30
Final Exam
1
30
30
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques.

4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.

X
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.

X
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions

8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.

10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

12

To be able to speak a second foreign at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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