COURSE UNIT TITLE

: ARTIFICAL INTELLIGENCE AND ITS APPLICATIONS IN INDUSTRY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IND 3938 ARTIFICAL INTELLIGENCE AND ITS APPLICATIONS IN INDUSTRY ELECTIVE 3 0 0 4

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FEHMI BURÇIN ÖZSOYDAN

Offered to

Industrial Engineering

Course Objective

This course aims to provide the students of the Department of Industrial Engineering with important artificial intelligence topics such as Artificial Neural Networks, Deep Learning, Hyper-heuristic learning and their production systems and mobile applications that have a very important place in engineering science. Thus, it is aimed to achieve professional gains in accordance with the Industry 4.0 era.

Learning Outcomes of the Course Unit

1   To understand the concept and importance of artificial intelligence
2   To be able to design, train and use artificial neural networks
3   To acquire the concept of Deep Learning
4   To acquire the concept of hyper-heuristic learning
5   To be able to use different artificial intelligence algorithms to train artificial neural networks
6   To have knowledge about machine learning

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to artificial intelligence: what is artificial intelligence, what are its uses
2 To comprehend the differences in the classification of artificial intelligence
3 Introduction to artificial neural networks
4 Artificial neural network model (supervised learning)
5 Artificial neural network model (supervised learning)
6 Multilayer artificial neural network model (supervised learning)
7 Neuroevolutionary algorithms
8 Neuroevolutionary algorithms
9 Midterm
10 Introduction to deep learning
11 Deep learning practices
12 Hyper-heuristic learning
13 Machine learning
14 Machine learning
15 Term project presentations

Recomended or Required Reading

Haykin, S., (2008) Neural Networks and Learning Machines, McMaster University, Hamilton, Ontario, Canada, ISBN-13: 978-0-13-147139-9, ISBN-10: 0-13-147139-2
Öztemel, E., (2016) Yapay Sinir Ağları, Papatya Yayıncılık
Çakır, F.S. (2018) Yapay Sinir Ağları, Matlab Kodları ve Matlab Toolbox Çözümleri, Nobel Akademik Yayıncılık, ISBN: 9786057928122
Rençberi Ö.F. (2018) Sınıflandırma Problemlerinde Çoklu Lojistik Regresyon, Yapay Sinir Ağ ve ANFIS Yöntemlerinin Karşılaştırılması: Insani Gelişmişlik Endeksi Üzerine Uygulama, Gazi Kitabevi ISBN: 6053446699

Planned Learning Activities and Teaching Methods

The topics covered in the course will be transferred to the students through computer-based applications, sample problem solutions and presentations on the board and students will be expected to perform these applications. The course will involve intensive coding. In addition, all the techniques described in this course will be brought together and used.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.20 + ASG * 0.30 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + ASG * 0.30 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm (20%) + Project (30%) + Final Exam (50%)

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Adress: Dokuz Eylül University, Industrial Engineering Department, Tınaztepe Campus, Izmir, Türkiye
E-mail: burcin.ozsoydan@deu.edu.tr, burcin.ozsoydan@gmail.com
Tel: 0232 301 7630

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Tutorials 0 0 0
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Preparation for quiz etc. 0 0 0
Preparing presentations 1 15 15
Final 1 2 2
Midterm 1 2 2
Quiz etc. 0 0 0
TOTAL WORKLOAD (hours) 102

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12
LO.1533544355
LO.2534545
LO.35555
LO.45445
LO.553553
LO.6525434345

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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