FACULTY OF ENGINEERING

Department of Computer 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 adequate knowledge in Mathematics, Science and Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

2

To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

3

To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively.

5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics.

6

To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary 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 global and social impact of Computer 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 Computer Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized 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

To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

12

To be able to speak a second foreign language 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 Computer Engineering.

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

 


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