COURSE UNIT TITLE

: INFORMATION TECHNOLOGIES FOR TEXTILE ENGINEERING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
TKS 4068 INFORMATION TECHNOLOGIES FOR TEXTILE ENGINEERING ELECTIVE 2 2 0 3

Offered By

Textile Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR YUNUS DOĞAN

Offered to

Textile Engineering

Course Objective

The main objective of this course is to give the theoretical content and applied examples of the technologies used in the field of informatics, as well as to create awareness of the concept of Machine Learning which is spreading rapidly in all sectors today.

Learning Outcomes of the Course Unit

1   1. Describing the basic concepts of web and mobile technologies as software applications.
2   2. Managing data and queries on database management systems.
3   3. Describing the basic concepts of Data Mining and Decision Support Systems
4   4. Describing the basic concepts of Artificial Intelligence and Machine Learning
5   5. Developing Machine Learning algorithms and applications basically.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to information technologies
2 Introduction to web software technologies
3 Web software technologies - II
4 Introduction to mobile software technologies
5 Introduction to Database Management Systems (DBMSs)
6 Data management and querying on DBMSs
7 Developing software applications integrated DBMSs
8 Introduction to Data Mining
9 Applications with Data Mining algorithms.
10 Introduction to Decision Support Systems and application
11 Introduction to Artificial Intelligence and Machine Learning
12 Machine Learning application
13 Machine Learning application - II
14 Machine Learning application - III

Recomended or Required Reading

Textbook(s): Mariya Yao, Adelyn Zhou and Marlene Jia, Applied Artificial Intelligence: A Handbook For Business Leaders , 2018
Supplementary Book(s): Oliver Theobald, Machine Learning for Absolute Beginners: A Plain English Introduction , 2017.

Planned Learning Activities and Teaching Methods

Lectures / Presentation
Guided problem solving
Laboratory exercises
Homework

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRJ PROJECT
4 FCG FINAL COURSE GRADE MTE * 0.20 + ASG * 0.30 + PRJ * 0.50


Further Notes About Assessment Methods

In-semester studies will be evaluated with a midterm exam and a number of laboratory / homework activities.
The final exam will cover all course topics.

Assessment Criteria

All of the following criteria will be evaluated with exams, homework and laboratory exercises.
1. Learnability of basic concepts will be evaluated with the correct understanding of given problem.
2. The following criteria will be considered during application design composition:
- Correct written and graphical representation of the algorithm
- Including sufficient comments
3. The following criteria should be provided during the translation from application design to program:
- The usage of available algorithms
- The comparisons of similar algorithms in problem solving
4. The following criteria should be considered during the implementation of the program:
- The usage of structured programming techniques
- The usage of sufficient data type
5. Producing significant / correct results of programs that developed for mathematical and other areas will be expected.

Language of Instruction

Turkish

Course Policies and Rules

1. Participation is mandatory (%70 theoretical courses and 80% practices)
2. Every cheating attempt will be finalized with disciplinary action.
3. Instructor reserves the right to quizzes. Notes should be added to these examinations, midterm and final exam grades.

Contact Details for the Lecturer(s)

Dr. Yunus Doğan
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35160 BUCA/IZMIR
Tel: +90 (232) 301 74 19
e-mail: yunus@deu.edu.tr

Office Hours

Tuesday 9:00-12:00
Wednedsday 9:00-12:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparing assignments 3 3 9
Preparation for midterm exam 1 3 3
Preparation for final exam 1 4 4
Preparing presentations 3 3 9
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 87

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.14
LO.25355
LO.3
LO.4
LO.55455