FACULTY OF ENGINEERING

Department of Mechatronics 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
Service Course
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
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 knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

2

To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

3

To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

4

To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

5

To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

6

To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both in oral and written forms; 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 global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

12

To be able to use the second foreign language at intermediate level.

13

To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

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

 


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